Security Vision - User contributions [en]

Template:Unknown Entities

2022-05-06T13:18:24Z

Andre:

<noinclude>
'''Template Summary:''' Adds links to create Unknown entities of all relevant categories:
* [[:Category:Dataset]]
* [[:Category:Deployments]]
* [[:Category:Events]]
* [[:Category:Institution]]
* [[:Category:Person]]
* [[:Category:Products]]

'''Invoked with:''' <nowiki>{{Unknown_Entities}}</nowiki>

Invokes: [[Template:Unknown_Entity_redlink]]

Edit in order to see Template
</noinclude><includeonly>=== Create Unknown Entities===
* {{Unknown_Entity_redlink|Dataset}}
* {{Unknown_Entity_redlink|Deployments}}
* {{Unknown_Entity_redlink|Events}}
* {{Unknown_Entity_redlink|Institution}}
* {{Unknown_Entity_redlink|Person}}
* {{Unknown_Entity_redlink|Products}}
</includeonly>

Category:Dataset

2022-05-06T13:08:20Z

Andre:

<PageSchema>
<pageforms_Form name="Dataset">
<CreateTitle>New Dataset</CreateTitle>
<EditTitle>Edit Dataset</EditTitle>
<standardInputs freeTextLabel="Free text" inputFreeText="1" inputWatch="1" inputSave="1" inputChanges="1" inputCancel="1"/>
</pageforms_Form>
<Template name="Dataset" format="infobox">
<Field name="Developed by People" list="list" display="nonempty">
<Label>Developed by (People)</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Developed by Institution" list="list" display="nonempty">
<Label>Developed by</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Creation Date">
<Label>Creation Date</Label>
<pageforms_FormInput>
<InputType>datepicker</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Dataset Category" list="list">
<Label>Dataset Category</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="URL">
<Label>URL</Label>
<pageforms_FormInput>
<InputType>text</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Keywords" list="list">
<Label>Keywords</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Related Technology" list="list">
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="has funding">
<Label>has funding</Label>
<pageforms_FormInput/>
<semanticmediawiki_Property name="">
<Type>Quantity</Type>
</semanticmediawiki_Property>
</Field>
<Field name="has images">
<Label>has images</Label>
<pageforms_FormInput/>
<semanticmediawiki_Property name="">
<Type>Number</Type>
</semanticmediawiki_Property>
</Field>
<Field name="has individuals">
<Label>has individuals</Label>
<pageforms_FormInput/>
<semanticmediawiki_Property name="">
<Type>Number</Type>
</semanticmediawiki_Property>
</Field>
<Field name="runs database software" list="list">
<Label>runs database software</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="runs search software" list="list">
<Label>runs search software</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="has full name">
<Label>Dataset full name</Label>
<pageforms_FormInput/>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
<Field name="is dataset category">
<Label>Dataset Category</Label>
<pageforms_FormInput>
<InputType>tokens</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Text</Type>
</semanticmediawiki_Property>
</Field>
<Field name="Country">
<Label>Country</Label>
<pageforms_FormInput>
<InputType>combobox</InputType>
</pageforms_FormInput>
<semanticmediawiki_Property name="">
<Type>Page</Type>
</semanticmediawiki_Property>
</Field>
</Template>
<Section name="Description" level="2"/>
</PageSchema>
{{#ask:
[[Category:Dataset]]
|? = Entry name
|? Country
|? Developed by
|? Dataset Category = Contents
|? Creation Date
|? is dataset category = Type
|? runs database software = DB Software
|? Developed by People = Developpers
|? Funding
|? has images = Nb Images
|? has individuals = Nb Individuals
|? runs database software = DB Software
|? runs search software = Search Software
|?Has subobject#-
|headers=plain
|mainlabel=-
}}

User:Andre

2022-05-06T12:55:35Z

Andre:

[[Special:Browse/ABIS_(Deployment,_Croatia)#_e79cd79ebb3f6de7e9a43ac33a5fabca]]

Pages with research & technical notes

* [[Help:Statements About Statements]]
* [[Help:Cite]]
* [[Help:Unknown Entities Pages]]

User:Andre

2022-05-06T12:54:23Z

Andre:

[[Special:Browse/ABIS (Croatia)#_a9605743d65f13eefed186770a4af0b4]]

Pages with research & technical notes

* [[Help:Statements About Statements]]
* [[Help:Cite]]
* [[Help:Unknown Entities Pages]]

Help:Uncertain information

2022-05-06T08:41:17Z

Andre:

__TOC__

=Forms with Uncertain information section=
'''In Forms+templates Uncertain information is expressed in a"Uncertain information" section.
'''

The "Uncertain information" section contains an invocation of the '''[[Template:Subobject Uncertain Information]]''' ie from [[Form:Deployments]]
<pre>
== Uncertain information ==

{{{for template|Subobject Uncertain Information|multiple|add button text=Add uncertain information}}}
* '''Field:''' {{{field|Propertyname|input type=dropdown|max values=1|values=City,Country,Managed by,Used by,Involved Entities,Datasets Used|mandatory}}}
* '''Value:''' {{{field|value|input type=text|mandatory}}}
* '''Certainty:''' {{{field|Certainty|default=Rumoured|input type=dropdown|values=Rumoured,Speculative|mandatory}}}
* '''Citekey of source:''' {{{field|Citekey|input type=combobox|values={{#ask:[[-Citation resource::+]]|?Citation key=|mainlabel=-|limit=10000|order by=Citation key}} }}}
* '''Description:''' {{{field|Description}}}
{{{end template}}}
</pre>
* in the ''Field:'' field the "values=" parameter determines the option of uncertain field/property
* the ''Value:'' field determines the the value for a given the chosen field
* the ''Certainty'' field defines the value of certainty

* the "for template" multi sub-form allows for multiple "Uncertain fields" to be created in a single form page
* the [[Template:Subobject Uncertain Information]] creates a [https://www.semantic-mediawiki.org/wiki/Subobject SMW subobject] with the [[:Category:Uncertain Information]]
* The contents of '''a subobject are not visible in the page which holds it, but can be displayed with a query''', which is done in the [[Template:Subobject Uncertain Information]]
** Note: '''A compound query, is used instead of an ask query''', to display the values (via query) of the Uncertain Information subobject, since it uses the Cite extension for citation, hence the query has to be done, both the the suboject and references (read more about querying Cite values in https://github.com/SemanticMediaWiki/SemanticCite/issues/95)

===Uses===
* [[Form:Deployments]]
* [[Form:Dataset]]

===Examples===
* [[TestDataset]]

=Qualifiers with Record (datatype) Property=
Previously qualifiers and other statements about statements were implemented by [[User:Ruben]] using Properties with Record type, such as [[Property:Potentially used by]] with fields [[Property:Institution]], [[Property:Information Certainty]], [[Property:Citation reference]], [[Property:Description]]

Example:
<nowiki>[[Potentially used by::TestInstitution;Speculative]]</nowiki>

Deployments Form and Template invokes [[Template:InstitutionCertainty]] to create the record.

Help:Uncertain information

2022-05-06T08:40:31Z

Andre: /* Uses */

__TOC__

=Forms with Uncertain information section=
'''In Forms+templates Uncertain information is expressed in a"Uncertain information" section.
'''

The "Uncertain information" section contains an invocation of the '''[[Template:Subobject Uncertain Information]]''' ie from [[Form:Deployments]]
<pre>
== Uncertain information ==

{{{for template|Subobject Uncertain Information|multiple|add button text=Add uncertain information}}}
* '''Field:''' {{{field|Propertyname|input type=dropdown|max values=1|values=City,Country,Managed by,Used by,Involved Entities,Datasets Used|mandatory}}}
* '''Value:''' {{{field|value|input type=text|mandatory}}}
* '''Certainty:''' {{{field|Certainty|default=Rumoured|input type=dropdown|values=Rumoured,Speculative|mandatory}}}
* '''Citekey of source:''' {{{field|Citekey|input type=combobox|values={{#ask:[[-Citation resource::+]]|?Citation key=|mainlabel=-|limit=10000|order by=Citation key}} }}}
* '''Description:''' {{{field|Description}}}
{{{end template}}}
</pre>
* in the ''Field:'' field the "values=" parameter determines the option of uncertain field/property
* the ''Value:'' field determines the the value for a given the chosen field
* the ''Certainty'' field defines the value of certainty

* the "for template" multi sub-form allows for multiple "Uncertain fields" to be created in a single form page
* the [[Template:Subobject Uncertain Information]] creates a [https://www.semantic-mediawiki.org/wiki/Subobject SMW subobject] with the [[:Category:Uncertain Information]]
* The contents of **a subobject are not visible in the page which holds it, but can be displayed with a query**, which is done in the [[Template:Subobject Uncertain Information]]
** Note: **A compound query, is used instead of an ask query***, to display the values (via query) of the Uncertain Information subobject, since it uses the Cite extension for citation, hence the query has to be done, both the the suboject and references (read more about querying Cite values in https://github.com/SemanticMediaWiki/SemanticCite/issues/95)

===Uses===
* [[Form:Deployments]]
* [[Form:Dataset]]

===Examples===
* [[TestDataset]]

=Qualifiers with Record (datatype) Property=
Previously qualifiers and other statements about statements were implemented by [[User:Ruben]] using Properties with Record type, such as [[Property:Potentially used by]] with fields [[Property:Institution]], [[Property:Information Certainty]], [[Property:Citation reference]], [[Property:Description]]

Example:
<nowiki>[[Potentially used by::TestInstitution;Speculative]]</nowiki>

Deployments Form and Template invokes [[Template:InstitutionCertainty]] to create the record.

Help:Uncertain information

2022-05-06T08:37:04Z

Andre:

__TOC__

=Forms with Uncertain information section=
'''In Forms+templates Uncertain information is expressed in a"Uncertain information" section.
'''

The "Uncertain information" section contains an invocation of the '''[[Template:Subobject Uncertain Information]]''' ie from [[Form:Deployments]]
<pre>
== Uncertain information ==

{{{for template|Subobject Uncertain Information|multiple|add button text=Add uncertain information}}}
* '''Field:''' {{{field|Propertyname|input type=dropdown|max values=1|values=City,Country,Managed by,Used by,Involved Entities,Datasets Used|mandatory}}}
* '''Value:''' {{{field|value|input type=text|mandatory}}}
* '''Certainty:''' {{{field|Certainty|default=Rumoured|input type=dropdown|values=Rumoured,Speculative|mandatory}}}
* '''Citekey of source:''' {{{field|Citekey|input type=combobox|values={{#ask:[[-Citation resource::+]]|?Citation key=|mainlabel=-|limit=10000|order by=Citation key}} }}}
* '''Description:''' {{{field|Description}}}
{{{end template}}}
</pre>
* in the ''Field:'' field the "values=" parameter determines the option of uncertain field/property
* the ''Value:'' field determines the the value for a given the chosen field
* the ''Certainty'' field defines the value of certainty

* the "for template" multi sub-form allows for multiple "Uncertain fields" to be created in a single form page
* the [[Template:Subobject Uncertain Information]] creates a [https://www.semantic-mediawiki.org/wiki/Subobject SMW subobject] with the [[:Category:Uncertain Information]]
* The contents of **a subobject are not visible in the page which holds it, but can be displayed with a query**, which is done in the [[Template:Subobject Uncertain Information]]
** Note: **A compound query, is used instead of an ask query***, to display the values (via query) of the Uncertain Information subobject, since it uses the Cite extension for citation, hence the query has to be done, both the the suboject and references (read more about querying Cite values in https://github.com/SemanticMediaWiki/SemanticCite/issues/95)

===Uses===
* [[Form:Deployments]]
* [[Form:Datasets]]

===Examples===
* [[TestDataset]]

=Qualifiers with Record (datatype) Property=
Previously qualifiers and other statements about statements were implemented by [[User:Ruben]] using Properties with Record type, such as [[Property:Potentially used by]] with fields [[Property:Institution]], [[Property:Information Certainty]], [[Property:Citation reference]], [[Property:Description]]

Example:
<nowiki>[[Potentially used by::TestInstitution;Speculative]]</nowiki>

Deployments Form and Template invokes [[Template:InstitutionCertainty]] to create the record.

Facial Recognition in Greece (Law Enforcement)

2022-05-03T12:31:21Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2019/01/01|Documented}}|City=Athens
|Country=Greece
|managed by=Video and Image Laboratory of the Audiovisual Evidence of the Department of Photography and Modus Operandi of the Hellenic Police Forensic Science Division, Hellenic Police Forensic Science Division
|Software Deployed=Fire Exos II
|Keywords=Facial Recognition
}}
==Description==
<blockquote>At the present time, FR is carried out by forensic examiners of the Video and Image Laboratory of the Audiovisual Evidence of the Department of Photography and Modus Operandi of the Hellenic Police Forensic Science Division. The number of examiners is 4 (with 2 licences in use).

The Fire Exos II software from Unidas is used for both database management and for the searches.

Only frontal view images are used for FR. A probe image can be processed (with Photoshop) either before or after it has been enrolled by using filters, cropping etc. Both possibilities (processing before and after enrolling) are allowed and used in practice.

There are no strict rules set for the number of posts in the candidate list. The length of the list depends on the quality of the probe image as well as on each individual examiner. Thus, hundreds of candidates may need to be examined. There is no standardised threshold set for the search results and the criteria for a "match" is decided by the examiner. Also, the "lights out" scenario is not used.

The search results are reported as a "match", "likely candidate" or "no result". The most common reported result is "likely candidate". The results are reported to the prosecutors and the police for investigative purposes. However, the reported information can be used in court as evidence.

At the time of the interview, the FR system had been used for less than a year and therefore no annual statistics were available relating to the number of searches, the number of hits and the match rate.

Photographs from civil databases can be used for manual 1:1 facial image comparisons, but not for database searches.[[CiteRef::sirgmetsSummaryReportProject2021]]</blockquote>
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
--- end citebot ---

Facial Recognition in Anderstorp Upper Secondary School (Skelleftea, Sweden)

2022-05-03T12:31:20Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Has event={{HasEvent|Start|08/2018|Documented}}|City=Skelleftea
|Country=Sweden
|managed by=Skelleftea Municipality
|Involved Entities=Swedish Authority for Privacy Protection (IMY)
|Datasets Used=Dataset of School Students
|Software Deployed=Unknown Live AFR (Authentication)
}}
==Description==
<blockquote>The Swedish Data Protection Authority (DPA) fined the Skelleftea municipality 200,000 Swedish Krona (£16,800, $20,700) for flouting a privacy law.

The trial involved tracking 22 students over three weeks and detecting when each pupil entered a classroom.

This is the first time that Sweden has ever issued a fine under GDPR.

The General Data Protection Regulation, which came into force last year, classes facial images and other biometric information as being a special category of data, with added restrictions on its use.

The DPA indicated that the fine would have been bigger had the trial been longer.

According to technology magazine ComputerSweden, Swedish authorities decided to investigate after reading media reports of Anderstorp's High School's trial. [[CiteRef::bbcnewsFacialRecognitionSchool2019]]

The local authority told Swedish state broadcaster SVT Nyheter in February that teachers had been spending 17,000 hours a year reporting attendance, and the authority had decided to see whether facial-recognition technology could speed up the process.</blockquote>
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE627803b1a5e791bb53493a01842f9011]]
--- end citebot ---

Biometric Cameras at Prague Airport

2022-05-03T12:31:20Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Information Certainty=Documented
|CiteRef=iureBigBrotherPrague2021
|Deployment Status=Ongoing
|Deployment Type=Biometric Cameras
|Has event={{HasEvent|Start|2018-01-01|Documented}}|City=Prague
|Country=Czech Republic
|managed by=Prague airport
|used by=Unknown Institution 0038
|Datasets Used=Unknown Dataset 0007
|Software Deployed=Unknown Technology 0043
}}

Security cameras Mechelen-Willebroek

2022-05-03T12:31:20Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2015|Documented}}|City=Mechelen, Willebroek
|Country=Belgium
|used by=Mechelen-Willebroek Police Department
|Software Deployed=Unknown Object Tracking Software, Unknown Event Detection Software, Unknown Avigilon Software
|Keywords=Event Detection, Object Detection, ANPR
|Included in graph=Greens Report
}}
<blockquote>
Intelligente monitoring

Op sites met honderden camera’s volstaan mensen niet om de beelden te monitoren. Met video-intelligentie analyseert uw systeem de beelden onafgebroken en geven ze bij voorafbepaalde situaties een signaal. Daarna kan u beslissen welke actie er moet genomen worden. [[CiteRef::nextelSlimmeCameraWaken]]
</blockquote>

<blockquote>
“Als je nu op het grondgebied van Mechelen en Willebroek alle camera’s gaat tellen, dus zowel degene geplaatst in het kader van videobewaking als die voor automatische nummerplaatherkenning, zitten we al aan een totaal van een [[Cameras used::530]]-tal camera’s”, vertelt Daniel Dumoulin. Dankzij analytische software kunnen deze slimme camera’s ook beelden analyseren en interpreteren. Zo tellen ze bijvoorbeeld voertuigen, detecteren ze verdacht gedrag en volgen ze personen of voertuigen. [[CiteRef::nextelSlimmeCameraWaken]]
</blockquote>

[[File:Screenshot Nextel in Mechelen.png|thumb|This is a screenshot of the Nextel promotion video with the Mechelen-Willebroek police zone, showing the use of some Avigilon software on the top left monitor. ]]
See also: https://player.vimeo.com/video/306997339?app_id=122963
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE334e39e19661a5771fe192036b2480f0]]
--- end citebot ---

[[Provided by::Nextel]]

HDR (Deployment, Lithuania)

2022-05-03T12:31:19Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2019/11/07|Documented}}|City=Vilnius
|Country=Lithuania
|managed by=Ministry of Interior (Lithuania)
|Datasets Used=HDR (Lithuania)
|Software Deployed=NeoFace Watch
|used by=Lithuanian Criminal Police Bureau, Lithuanian State Border Guard Service
}}

[[CiteRef::sirgmetsSummaryReportProject2021]]
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
--- end citebot ---

BDAS Deployment (Latvia)

2022-05-03T12:31:19Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2012|Documented}}|City=Riga
|Country=Latvia
|managed by=Ministry of Interior (Latvia)
|used by=State Police (Latvia)
|Datasets Used=BDAS
|Software Deployed=MorphoTrust ABIS Search Engine, Software from RIX Technologies
}}
Sources and information are stored in the [[BDAS]] page

[[CiteRef::sirgmetsSummaryReportProject2021]]
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
--- end citebot ---

EDE (AFR used by Austrian Criminal Intelligence Service)

2022-05-03T12:31:19Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2020/08/01|Documented}}|City=Vienna
|Country=Austria
|managed by=Ministry of the Interior (Austria), Austrian Criminal Intelligence Service
|Datasets Used=Criminal identification Database (Erkennungsdienstliche Evidenz), Central register of foreigners (Integriertes Zentrales Fremdenregister)
|Software Deployed=Cognitec Face VACS DBScan
}}
Austria started implementing facial recognition in August 2020 using the so-called criminal identification database containing about 1,250,000 images of criminals, missing persons, dead bodies, and crime scenes. The facial recognition system was produced by [[Involved Entities::Cognitec Systems]] and implemented by [[Involved Entities::Atos|Atos IT Solutions]]. Solely the Criminal Intelligence Service can use the database for searches in the context of criminal investigations. Including the criminal investigation departments of the federal states is planned, however. Investigators and police need to request such a search made by experts at the Criminal Intelligence Service. The inclusion of civil databases such as driver’s licenses or social security register is permitted. The police can request these pictures for investigations. Two databases are concerned: the criminal identification Database (Erkennungsdienstliche Evidenz, EDE) and the central register of foreigners (Integriertes Zentrales Fremdenregister, IZR), both of which are maintained and owned by the Federal Ministry of Interior. Legally it would be possible to search both databases during a facial recognition search. Technically, however, only EDE can be used. EDE includes personal data, criminal offences, date and origin of the biometric data. Furthermore, EDE is connected to databases that provide DNA information, fingerprints, images of an offender, missing persons or dead bodies, and crime scene images (TELEFI 2021). The ministry of the interior maintains 13 databases with pictures, including two analysis platforms..

The association Epicenter.works criticizes the system as it is unclear where the significant amount of data for the reference database would come from. Furthermore, the system can be filtered for metadata such as the origin of an individual, which might lead to racial profiling and discrimination (Epicenter.works 2019). The facial recognition software was used during protests, for instance, during a confrontation of Kurdish activists with Turkish nationalists in Vienna. The facial recognition software was used to identify suspects during the altercations of protestors in June and July 2020 (Sulzbacher 2020).

Epicenter.works (17.07.2019). Gesichtserkennung in Österreich: woher stamen die Daten? Retrieved on 18 April 2021 from https://epicenter.works/content/gesichtserkennung-in-oesterreich-woher-stammen-die-daten

Sulzbacher, Markus (15.09.2020). Polizei nutzt neue Gesichtserkennung, um Demonstranten zu identifizieren. Retrieved on 18 April 2021 from https://www.derstandard.at/story/2000119996329/polizei-nutzt-neue-gesichtserkennung-um-demonstranten-zu-identifizieren

TELEFI Project (January, 2021). Summary Report of the project “Towards the European Level Exchange of Facial Images”. Retrieved on 18 April 2021 from https://www.telefi-project.eu/sites/default/files/TELEFI_SummaryReport.pdf

[[CiteRef::sirgmetsSummaryReportProject2021]]
--- By citebot ---

This page uses the following references:

* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
--- end citebot ---

Safe City Malta

2022-05-03T12:31:18Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Has event={{HasEvent|Start|04/2016|Documented}}|City=Paceville, Marsa
|Country=Malta
|managed by=Maltese State
|Involved Entities=Huawei
|Datasets Used=Unknown Face Database
|Software Deployed=Unknown Live AFR from Huawei
|Keywords=Facial Recognition
|Included in graph=Greens Report
}}
<blockquote>
This new form of surveillance technology will utilise Artificial Intelligence (AI) controlled technology to monitor activity and to identify subjects though the use of algorithms that will, supposedly, match the faces of people with established databases. This project will be rolled out and developed by the Maltese State in cooperation with the Chinese tech-giant Huawei.
[[CiteRef::zammitSafeCityMalta2019]]
</blockquote>

<blockquote>[Safe City Malta director Joe Cuschieri] added that Huawei would only be providing hardware solutions. “Huawei will not be operating any Safe City equipment, and will not even have direct access for technical support purposes.” “Data will be stored in Malta and will stay in Malta, governed by a security and data retention policy.” [[CiteRef::paceHuaweiNotOperating2019]]</blockquote>

<blockquote>
The United Nations’ rapporteur on data protection, Professor Joseph Cannataci, who is based in Malta, filed a request for clarifications on the project immediately after a pilot of the Safe City system was announced for Paceville in 2017. Malta Today reports that legal and data protection issues appear to have sidetracked those plans.
[[CiteRef::burtEUCommissionerWarns2019]]
</blockquote>

<blockquote>
The UN Special Rapporteur on the right to privacy is recommending to the Government of Malta to reinforce the protection of fundamental human rights and respect for the rule of law in the context of the reform of its surveillance, policy, legislation and practices.
[[CiteRef::unricitaliaMaltaExpertRecommends2019]]
</blockquote>

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German central criminal information system INPOL

2022-05-03T12:31:18Z

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2011/01/01|Documented}}|City=Berlin
|Country=Germany
|managed by=German Federal Criminal Police Office (Bundeskriminalamt)
|Datasets Used=German central criminal information system INPOL
|Software Deployed=Cognitec Face VACS DBScan, Oracle Software
}}
Germany has been using automated facial recognition technologies to identify criminal activity since 2008 using a central criminal information system called INPOL (Informationssystem Polizei), maintained by the Bundeskriminalamt (BKA), which is the federal criminal police office. INPOL uses Oracle Software and includes the following information: name, aliases, date and place of birth, nationality, fingerprints, mugshots, appearance, information about criminal histories such as prison sentences or violence of an individual, and DNA information. However, DNA information is not automatically recorded (TELEFI 2021).

The INPOL database includes facial images of suspects, arrestees, missing persons, and convicted individuals. For the purpose of facial recognition, anatomical features of a person's face or head as seen on video surveillance or images are used as a material to match with data in INPOL. The facial recognition system compares templates and lists all the matches ordered by degree of accordance. The BKA has specific personnel visually analysing the system's choices and providing an assessment, defining the probability of identifying a person. This assessment can be used in a court of law if necessary (Bundeskriminalamt, n.d.b). Searches in the database are conducted by using Cognitec Face VACS software (TELEFI 2021).

As of March 2020, INPOL consists of 5,8 million images of about 3,6 million individuals. All police stations in Germany have access to this database. The BKA saves biometric data and can be used by other ministries as well, for instance, to identify asylum seekers. Furthermore, the data is shared in the context of the Prüm cooperation on an international level (mostly fingerprints and DNA patterns). Furthermore, the BKA saves DNA analysis data as part of INPOL, accessible for all police stations in Germany. That database contains 1,2 million data sets (Bundeskriminalamt, n.d.a). Other recorded facial images, for instance, driver’s licenses or passports, are not included in the search, and the database is mainly used for police work (TELEFI 2021).

Bundeskriminalamt (n.d.a). Erkennungsdienst. Retrieved on 18 April 2021 from https://www.bka.de/DE/UnsereAufgaben/Ermittlungsunterstuetzung/Erkennungsdienst/erkennungsdienst_node.html;jsessionid=692BF71E167806F9A74B3147206BC657.live2302[[CiteRef::bundeskriminalamtBKAErkennungsdienst2021]]

Bundeskriminalamt (n.d.b). Gesichtserkennung. Retrieved on 18 April 2021 from https://www.bka.de/DE/UnsereAufgaben/Ermittlungsunterstuetzung/Kriminaltechnik/Biometrie/Gesichtserkennung/gesichtserkennung_node.html[[CiteRef::bundeskriminalamtBKAGesichtserkennung2021]]

TELEFI Project (January, 2021). Summary Report of the project “Towards the European Level Exchange of Facial Images”. Retrieved on 18 April 2021 from https://www.telefi-project.eu/sites/default/files/TELEFI_SummaryReport.pdf[[CiteRef::sirgmetsSummaryReportProject2021]]
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* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
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Citybeacons Eindhoven

2022-05-03T12:31:17Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Analytics, Surveillance
|Has event={{HasEvent|Start|2017|Documented}}|City=Eindhoven
|Country=Netherlands
|managed by=Citybeacon
|used by=Eindhoven247
|Datasets Used=No Dataset Used
|Software Deployed=ViSense (ViNotion)
|Included in graph=Greens Report
}}
See also: https://datastudio-eindhoven.nl/ and https://www.onomatopee.net/exhibition/eindhoven-footnotes/

<blockquote>
Was dat het? Nee, nog lang niet, lacht de manager. “Er zitten ook camera’s in, die voldoen aan de kwaliteitseisen van de politie. Inclusief twee audience tracking camera’s. Die kunnen een heatmap maken en zo een druktebeeld genereren. Ze kunnen ook onderscheid maken tussen mannen en vrouwen, leeftijd. En of iemand goedgeluimd is of niet.”
[[CiteRef::vandijkSmartSocietyEindhoven2017]]
(2021-05-06)
</blockquote>

<blockquote>
ViNotion feliciteert @psv met de 24e landstitel! Speciaal voor de huldiging stellen wij onze CrowdDynamics analyse beschikbaar. Hiermee geven wij inzicht in de bezoekersstromen om de veiligheid van de PSV-fans te vergroten. Tevens meten wij de drukte in het centrum. Op initiatief van Eindhoven 247 wordt deze techniek vandaag experimenteel ingezet i.s.m. City Beacon. Onze technologie wordt 24/7 gebruikt in het Stratumseind 2.0 project voor crowd management. 📸: Twicer. . . #vinotion #psv #huldiging #crowdmanagement #gemeenteeindhoven #citybeacon #eindhoven247 #crowddynamics #visense #smartcameras #eindhoven #landskampioen #eindhoven

https://www.picuki.com/tag/vinotion (2021-05-06)
</blockquote>
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Smart surveillance coupled with audio recognition (Strasbourg)

2022-05-03T12:31:17Z

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{{Deployments
|Has event={{HasEvent|Start|01/2015|Documented}}|City=Strasbourg
|Country=France
|managed by=City of Strasbourg
|used by=Strasbourg Municipal Police
|Software Deployed=Video Synopsis (Briefcam) - Probably, Unknown Audio Recognition Software (Strasbourg)
|Managed by=Ville de Strasbourg
}}
<blockquote>Installation expérimentale de « capteurs sonores » dans le cadre de la mission Bruits et Nuisances sonores en 2015. Ces micros sont couplés au caméras de vidéosurveillance.
Dans le Pixel de l'AN2V de 2020, cette « expérimentation » semble toujours être d'actualité. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

=== Additional properties ===

* Geolocation [[Geolocation::48.58216435291874°,7.75055876396386°]]
* CiteRef [[CiteRef::technopoliceFrMarch2020]]
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ABIS (Deployment, Estonia)

2022-05-03T12:31:17Z

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Planned
|Deployment Type=Criminal investigations, Other applications
|Has event={{HasEvent|Start|2021-03-01|Documented}}|Has event={{HasEvent|Start|2021/03/01|Documented|sirgmetsSummaryReportProject2021}}
|City=Talinn
|Country=Estonia
|managed by=MInistry of Interior (Estonia), Ministry of Justice (Estonia)
|used by=Estonian Police and Border Guard Board, Estonian Forensic Science Institute
|Datasets Used=Criminal-ABIS (Estonia), Civil-ABIS (Estonia), ITDAK, ETR, KODAK, SKEELD, LTR, ILLEGAAL2, RAKS, Visa Register (Estonia), Motor register (Estonia)
|Software Deployed=Unknown Multi-Biometric Identification Software by Idemia
|Included in graph=Greens Report
}}
[[CiteRef::sirgmetsSummaryReportProject2021]]
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Madrid Estacion Sur

2022-05-03T12:31:16Z

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{{Deployments
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2016|Documented}}|City=Madrid
|Country=Spain
|Software Deployed=Herta Security Facial Recognition
}}
==Description==
<blockquote>A un proyecto piloto anunciado a finales del año pasado para pagar con la cara en '''varios autobuses municipales de Madrid''' (el usuario se da de alta en una web y al acceder al autobús una cámara le identifica y le carga automáticamente el importe del billete en su cuenta) se unió, también en la capital de España, el reconocimiento facial en la '''Estación Sur''' para identificar a delincuentes. Se comenzó a implantar en 2016 y según sus responsables ha servido para reducir la actividad de los carteristas en esta estación de autobuses.[[CiteRef::sevillaReconocimientoFacialQue2020]]</blockquote><blockquote>Una de las pocas empresas madrileñas que ha revelado por iniciativa propia que usa cámaras con reconocimiento facial es la gestora de la Estación Sur de Autobuses. Sus responsables han sido hasta ahora los portavoces más entusiastas en Madrid de este tipo de tecnología porque dicen que ha servido para espantar a carteristas y otros delincuentes. En 2010, la estación tenía cinco incidentes al día. En todo 2019 hasta julio solo había tenido siete.[[CiteRef::peinadoCamarasQueLeen2019]]</blockquote>Interest of other actors for the "Madrid Estacion Sur" example<blockquote>“Fue un antes y un después”, dice Miguel Ángel Gallego, que fue durante todo ese período su director de seguridad. Recuerda que cuando desapareció Diana Quer, en 2016, aún no tenían la tecnología completamente instalada en la estación. Su equipo invirtió dos jornadas de trabajo en revisar algunas grabaciones de pasajeros que llegaban en autobuses procedentes de Galicia en los que se pensó que la joven podía haber regresado a Madrid. Con cámaras y programas de reconocimiento facial la comprobación habría durado unos minutos. “Fue un tiempo que los agentes se podían haber ahorrado cerrando esa línea de investigación”, dice Gallego..[[CiteRef::peinadoCamarasQueLeen2019]]</blockquote><blockquote>Gallego, que hace una semana dejó su empleo en la Estación Sur para trabajar en Renfe, dice que en los últimos meses ha pasado por su sala de control “la flor y nata de la seguridad en Madrid, todos ávidos de saber cómo funciona una tecnología que, agrega Gallego, va a ser nuestro futuro..[[CiteRef::peinadoCamarasQueLeen2019]]</blockquote>

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AFIS (Deployment, Italy)

2022-05-03T12:31:16Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2017|Documented}}|City=Rome
|Country=Italy
|managed by=Ministry of Interior (Italy)
|used by=Polizia di Stato, Carabinieri
|Datasets Used=AFIS, SARI
|Software Deployed=Reco - Face recognition system, NeoFace Watch
}}

[[CiteRef::sirgmetsSummaryReportProject2021]]
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ViSense - Mechelen

2022-05-03T12:31:16Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Analytics
|Has event={{HasEvent|Start|2014|Documented}}|City=Mechelen
|Country=Belgium
|Datasets Used=No Dataset Used
|Software Deployed=ViSense CrowdDynamics
}}
<blockquote>
ViSense stelt ons in staat om de aantrekkelijkheid van onze retail over tijd te volgen. De nauwkeurige gegevens maken het daarnaast mogelijk om ons winkelgebied te vergelijken met dat van andere steden.

- Bart Somers Burgemeester Mechelen, België [[CiteRef::ViSenseCrowdDynamics]]
</blockquote>

[[CiteRef::vanhaezendonckMechelenTeltRuim2015]]

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CATCH

2022-05-03T12:31:15Z

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{{Deployments
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2016|Documented}}|City=The Hague
|Country=Netherlands
|managed by=Centrum voor Biometrie
|used by=Dutch Police
|Involved Entities=Dutch Judicial Information Service (Justid)
|Datasets Used=Vreemdelingendatabank (NL), Dutch Criminal law chain database SKDB (Strafrechtsketendatabank)
|Software Deployed=Unknown Facial Recognition Software by Idemia
|Keywords=Facial Recognition, Database
}}
== Centrale Automatische TeChnologie voor Herkenning van personen ==

<blockquote>
De politie gebruikt foto's van meer dan [[has individuals::1300000|1,3 miljoen personen]] voor gezichtsherkenning, maar heeft geen idee welke mensen terecht in de gezichtendatabase staan. Zo is dus ook niet bekend bij wie dat niet langer het geval is, bijvoorbeeld na een vrijspraak. Dat blijkt uit onderzoek van NU.nl. Deskundigen betwijfelen of gezichtsherkenning bij de politie daardoor nog geoorloofd is.

[[CiteRef::hulsenTienduizendenMensenMogelijk2021]]
</blockquote>

<blockquote>
Riemen zegt dat het Centrum voor Biometrie de CATCH-database als beheerder "bijna onmogelijk" op orde kan houden. De politie heeft altijd al gesteld dat de regels van bewaartermijnen complex zijn, zegt hij. Ook heeft de politie geen zicht op hoe een zaak tegen een verdachte afloopt zodra het onderzoek wordt overgedragen aan het OM.

[[CiteRef::hulsenTienduizendenMensenMogelijk2021]]
</blockquote>

<blockquote>
De gezichtsfoto's in de CATCH-database komen voor een groot deel uit een andere databank met verdachten en veroordeelden. Het ministerie van Justitie en Veiligheid beheert dit persoonsregister om het strafrechtproces binnen de overheid te stroomlijnen. De wet die dat regelt is sinds 2010 van kracht.

Mensen komen in die databank terecht als ze verdacht worden van bepaalde relatief zware strafbare feiten. De gezichtsfoto's moeten verwijderd worden zodra iemand niet langer verdachte is. Voorwaarden zijn wel dat die persoon niet eerder veroordeeld is en dat er niet nog een andere zaak tegen diegene loopt.

[[CiteRef::hulsenTienduizendenMensenMogelijk2021]]
</blockquote>

<blockquote>
De Nederlandse politiedatabase met afbeeldingen van veroordeelden en verdachten van ernstige misdaden bevat inmiddels [[has images::2200000|2,2 miljoen afbeeldingen]] van 1,3 miljoen mensen. De politie gebruikt de database voor gezichtsherkenning.

[[CiteRef::miltenburgvanPolitiedatabaseBevatMiljoen2019]]
</blockquote>

## Procedure:

<blockquote>
De politie en de bijzondere opsporingsdiensten kunnen een aanvraag doen voor het zoeken naar de identiteit van een onbekende persoon door middel van Catch. Catch zoekt op basis van een biometrisch profiel van de kenmerken van het gezicht naar overeenkomsten en genereert een kandidatenlijst van gezichten die technisch het meest op de gezochte afbeelding lijken. Een expert kijkt naar dit resultaat. Als deze expert er van overtuigd is dat er sprake is van voldoende overeenkomst met één van de kandidaten uit de lijst, wordt de match voorgelegd aan twee andere experts die de overeenkomst onafhankelijk van elkaar beoordelen. Als beide experts tot dezelfde conclusie komen, dan wordt die gezamenlijke conclusie als eindconclusie gerapporteerd. Bij een ongelijke conclusie wordt de meest conservatieve conclusie gerapporteerd. Deze rapportage wordt door de politie als aanknopingspunt gebruikt voor vervolgonderzoek.De politie maakt gebruik van software van het bedrijf [[Involved Entities::IDEMIA]]. IDEMIA publiceert regelmatig over de doorontwikkeling van deze software. De testresultatenzijn openbaar.

[[CiteRef::grapperhausAntwoordenKamervragenHet2019]]
</blockquote>

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SARI (Deployment)

2022-05-03T12:31:15Z

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{{Deployments
|Deployment Status=Ongoing
|Has event={{HasEvent|Start|2018|Documented}}|City=Rome
|Country=Italy
|managed by=Ministry of Interior (Italy)
|used by=Polizia di Stato
|Datasets Used=AFIS
|Software Deployed=SARI Entreprise (Software)
}}
==Description==
<blockquote>Lo scorso gennaio, infatti, il Ministero dell’Interno ha provveduto ad aggiudicare all’azienda Parsec 3.26 la fornitura di un software per il “Sistema Automatico di Riconoscimento Immagini (SARI)” che, se vogliamo essere più precisi, è un sistema per il riconoscimento facciale. Secondo quanto riportato dal Quotidiano di Puglia, Parsec 3.26 è un’azienda leccese che finanzia e collabora con il centro di ricerca CNR ISASI “per lo sviluppo di algoritmi di riconoscimento facciale.” [[CiteRef::colucciniAncheItaliaSi2017]]</blockquote><blockquote>[Translated] What is Sari. Police experts explain that the Sari database contains 16 million faces that can be recognised by facial recognition algorithms. This does not mean, they point out, that there are 16 million people on file: each individual can have more than one photo. Importantly, the database does not contain photos of ordinary people who, for example, go shopping, but of individuals who have committed crimes and are therefore known. Just enter the picture of the person you are looking for into the database and Sari will quickly calculate a list of people who look like the face you are looking for. It should also be specified that, in order for the result obtained to be valid in court, a physiognomic comparison must be carried out by specialised personnel of the Scientific Police. "Sari is used following crimes - explains Giovanni Tessitore, Chief Technical Commissioner of the State Police - and when we have available, for example, the image of the face of a subject who has committed a crime, we can search for the same face, and therefore, its identity, within the database, thanks to artificial intelligence". [[CiteRef::tg24SariEccoCome]]</blockquote><blockquote>Come si legge nell’interrogazione firmata dal deputato Pd, Filippo Sensi, “il sistema di riconoscimento facciale da parte delle forze di polizia è utilizzato anche in Italia, almeno dal 2018.[[CiteRef::RiconoscimentoFaccialeItalia2020]]</blockquote><blockquote>Come già emerso dalla vicenda del 2018, il Viminale ha confermato nella risposta scritta che la Polizia di Stato utilizza e gestisce il Sari Enterprise. E precisa che: “nella banca dati Afis sono presenti, attualmente, 17.592.769 cartellini fotosegnaletici, acquisiti a norma di legge, corrispondenti a 9.882.490 individui diversi, di cui 2.090.064 si riferiscono a cittadini italiani. Viceversa, il Sari, essendo un software e non una banca dati, non contiene alcun dato”.[[CiteRef::RiconoscimentoFaccialeItalia2020]]</blockquote>

==SARI Entreprise & SARI Realtime==
<blockquote>Lo scorso gennaio, infatti, il Ministero dell’Interno ha provveduto ad aggiudicare all’azienda Parsec 3.26 la fornitura di un software per il “Sistema Automatico di Riconoscimento Immagini (SARI)” che, se vogliamo essere più precisi, è un sistema per il riconoscimento facciale. Secondo quanto riportato dal Quotidiano di Puglia, Parsec 3.26 è un’azienda leccese che finanzia e collabora con il centro di ricerca CNR ISASI “per lo sviluppo di algoritmi di riconoscimento facciale.”

Come si può leggere nel capitolato tecnico dell’appalto, SARI deve essere in grado di gestire due scenari operativi, Enterprise e Real-Time. Il primo è quello in cui “un operatore ha la necessità di ricercare l’identità di un volto presente in un’immagine, per mezzo di uno o più algoritmi di riconoscimento facciale, all’interno di una banca dati di grandi dimensioni.”

La banca dati in questione prevede 10 milioni di immagini e l’output dell’algoritmo deve essere una lista di volti simili a quello ricercato.

Per lo scenario Real-Time, invece, data un’area geografica ristretta, si ha bisogno di un sistema in grado di “analizzare in tempo reale i volti dei soggetti ripresi dalle telecamere ivi installate confrontandoli con una watch-list la cui grandezza è dell’ordine delle centinaia di migliaia di soggetti.”[[CiteRef::colucciniAncheItaliaSi2017]]</blockquote>

==Criticism==
<blockquote>In realtà, niente di più di quanto si sapesse già da oltre un anno. Come ha fatto notare Fabio Chiusi, giornalista esperto di tech, su Twitter dal dicastero non è emerso “nulla su quanto, come e con quali risultati viene usato il sistema” di riconoscimento facciale in Italia.[[CiteRef::RiconoscimentoFaccialeItalia2020]]</blockquote><blockquote>Quindi nulla su quanto, come e con quali risultati viene usato il sistema.

— Fabio Chiusi (@fabiochiusi) February 5, 2020[[CiteRef::RiconoscimentoFaccialeItalia2020]]</blockquote><blockquote>Nessuna risposta su utilizzo tecnologie riconducibili a Clearview AI in Italia, numeri impressionanti su banche dati e nessuna menzione per aziende esterne che possano maneggiare e conservare quei dati. Mi sono detto solo parzialmente soddisfatto #facialrecognition #GDPR pic.twitter.com/UkRn18nJEL

— nomfup (@nomfup) February 5, 2020[[CiteRef::RiconoscimentoFaccialeItalia2020]]</blockquote>

== Normativa ==
<blockquote>
=== I provvedimenti del Garante ===
Volendoci limitare in questa sede ai trattamenti di videosorveglianza connessi ad aspetti biometrici effettuate da soggetti pubblici, sono due i recenti provvedimenti del [[Garante per la Privacy]] italiano che si sono espressi sulla tematica, con particolare riferimento nel primo caso all’adozione del sistema SARI Enterprise da parte del Ministero dell’Interno e nel secondo di una piattaforma di videosorveglianza con funzioni di riconoscimento facciale nel Comune di Como.

Entrambi i casi hanno destato un certo scalpore nell’opinione pubblica e tra gli “addetti ai lavori”, anche per le '''conclusioni opposte''' cui i provvedimenti del Garante sono giunti.

Ed inverso '''nel primo caso''' il Garante, con provvedimento n. 440 del 26 luglio 2018 ha concluso per l’assenza di profili di illiceità nel trattamento operato dal Ministero con l’utilizzo del sistema '''SARI''' che – lo rammentiamo – essenzialmente prevede ad integrare le immagini provenienti da una fonte esterna (come ad esempio un sistema di videosorveglianza) con la base dati delle foto segnaletiche presenti nel sistema [[AFIS-SSA]], che consente di effettuare ricerche nell’archivio dei soggetti fotosegnalati (A.F.I.S.). L’uso di SARI Enterprise, nella valutazione effettuata dal Garante, non costituisce un '''diverso nuovo trattamento dei dati personali degli interessati''' (diverso rispetto al trattamento effettuato manualmente da parte degli operatori di PS tramite AFIS-SSA) in quanto essenzialmente viene valutato essere “semplicemente” una modalità (semi)-automatica di ricerca dove l’operatore umano viene sostituito dalla piattaforma che – con maggiore velocità e precisione – può ricercare nel proprio database delle eventuali corrispondenze con l’immagine immessa in input. Una volta operata questa ricostruzione circa la natura “non nuova” del trattamento, consegue che l’adozione di questo sistema ed il relativo trattamento si basa sulla stessa base giuridica del “trattamento manuale”, ossia il decreto legislativo 18 maggio 2018, n. 51, recante l’attuazione della direttiva (UE) 2016/680 del Parlamento europeo e del Consiglio, del 27 aprile 2016, relativa alla protezione delle persone fisiche con riguardo al trattamento dei dati personali da parte delle autorità competenti a fini di prevenzione, indagine, accertamento e perseguimento di reati o esecuzione di sanzioni penali, nonché alla libera circolazione di tali dati e che abroga la decisione quadro 2008/977/GAI del Consiglio nel combinato disposto di una pluralità di fonti nazionali tra cui l’art. 4 del T.U.L.P.S. e l’art. 7 del relativo regolamento di esecuzione; l’art. 349 del codice di procedura penale; l’art. 11 del decreto legge 21 marzo 1978, n. 59, convertito in legge 18 maggio 1978, n. 191; l’art. 5 del decreto legislativo 25 luglio 1998, n. 286.

In definitiva, il Garante conclude(va) che il trattamento in argomento costituisce, '''un mero ausilio all’agire umano''', avente lo scopo di velocizzare l’identificazione, da parte dell’operatore di polizia, di un soggetto ricercato della cui immagine facciale si disponga, '''ferma restando l’esigenza dell’intervento dell’operatore per verificare l’attendibilità dei risultati prodotti dal sistema automatizzato'''. In questo senso la conclusione dell’Autorità Granate sembra condivisibile nella misura in cui il sistema SARI Enterprise “semplicemente” compia in modo automatico quell’attività di identificazione dei dati biometrici salienti (ad es. colore dei capelli, altezza) a partire da un’immagine, parallelamente a quello che in precedenza l’operatore di PS faceva con una propria elaborazione manuale, di talché effettivamente si potrebbe parlare di diversa '''modalità''' di trattamento più che di '''diverso trattamento'''. E’ però interessante notare che il provvedimento del Garante non prende in considerazione il parallelo sistema [[SARI Real-Time]] che diversamente dal “cugino” Enterprise si preannuncia maggiormente sofisticato e apparentemente maggiormente invasivo in quanto si prefigge di permettere il riconoscimento '''in tempo reale''' di volti presenti in flussi video provenienti da telecamere IP, con relativo confronto di (tutti i) volti presenti nei flussi video con quelli di una “watch-list” (con una grandezza dell’ordine di 100.000 soggetti) e trasmissione di un “alert” in caso di “match positivo”. Rispetto a tale sistema è tutt’ora aperta un’istruttoria presso il Garante, coeva a quella di [[SARI Enterprise]]; il fatto che a distanza di due anni tale istruttoria non abbia ancora trovato conclusione è indice del fatto che la questione del monitoraggio di massa ed indiscriminato in tempo reale della popolazione è tutt’altro che di facile soluzione e lascia aperta una pluralità di questioni, in particolare sotto il profilo del principio di minimizzazione del trattamento (art. 20 Direttiva 2016/680).

'''Il secondo caso ricordato, relativo al Comune di Como''' (provvedimento n. 54 del 26 febbraio 2020), di converso è giunto ad una conclusione diametralmente opposta, ritenendo allo stato inapplicabile il corpus normativo, che pure ha legittimato SARI Enterprise, al contesto dei trattamenti effettuati dagli enti territoriali, ed ha statuito che “nel caso in esame non ricorrono le condizioni sussistenti invece nell’ambito del sistema SARI-enterprise oggetto del provvedimento del Garante del 26 luglio 2018” rinviando – ''de jure condendo'' – al “d.P.R. di prossima adozione di cui all’art. 5, comma 2, del d.lgs. n. 51/2018, così oltretutto uniformando le condizioni per il (e le garanzie nel) ricorso a dati biometrici da parte degli enti territoriali, in particolare per le funzioni di polizia giudiziaria riservate alla polizia locale.”

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In questo scenario in cui la '''videosorveglianza posta in essere da enti territoriali non pare aver ancora ricevuto un parere pienamente positivo dall’Autorità Garante''', desta perplessità la decisione dell’amministrazione della città di Torino di voler procedere all’attivazione del sistema Argo, che – sulla base delle informazioni ad oggi pubblicamente disponibili – non pare discostarsi sostanzialmente da quanto oggetto del provvedimento relativo al Comune di Como.

=== Il contesto europeo ===
Allargando la visuale dal livello nazionale a quello Europeo, non può non essere menzionato il documento dello European Data Protection Board ''Guidelines 3/2019 on processing of personal data through video devices'' del luglio 2019.

L’EDPB nel proprio documento evidenzia come il ricorso a tecnologie di trattamento di dati biometrici debba avvenire nel rispetto dei principi di legittimità, necessità, proporzionalità minimizzazione dei dati, come previsto dal GDPR. Se pure l’uso di queste tecnologie può essere percepito come particolarmente efficace, i titolari dei relativi trattamenti dovrebbero prima di tutto valutare l’impatto sui diritti e sulle libertà fondamentali e '''prendere in considerazione mezzi meno invasivi per raggiungere i loro obiettivi'''.

Nella misura in cui le riprese video vengono elaborate al fine di estrarre (meta)dati idonei alla identificazione di uno specifico individuo, gli stessi possono essere considerati a tutti gli effetti dati biometrici ai fini dell’art. 9 GDPR, nonché dell’art. 10 della Direttiva 2016/680 il che all’atto pratico limita fortemente la possibilità di un uso legittimo degli stessi, soprattutto da parte di soggetti pubblici.

=== Siamo sorvegliati ===
“Siamo sorvegliati. Il governo dispone di un sistema segreto, una Macchina, che ci spia ogni ora, di ogni singolo giorno. Lo so, perché l’ho costruita io. Ho ideato la Macchina per prevenire atti di terrorismo, ma vede ogni cosa. Crimini violenti che coinvolgono persone comuni, persone come voi. Crimini che il governo considera irrilevanti. E poiché loro non avrebbero agito, decisi di farlo io. Ma mi serviva un socio, qualcuno con le capacità per intervenire. Le autorità ci danno la caccia, lavoriamo in incognito. Non ci troverete mai. Ma che siate vittime o carnefici, se esce il vostro numero… noi troveremo voi.”

Con queste parole si aprono gli episodi della prima stagione della serie televisiva “'''Person of Interest'''” che, nel 2011, narrava la storia di come un sistema di sorveglianza (biometrica e non) di massa abbinato a tecnologie di IA potesse essere utilizzato nella '''lotta al crimine'''. La tematica è tutt’altro che nuova nella letteratura e nella cinematografia contemporanea: '''il genere distopico''' ha da sempre attinto a piene mani al tema dell’abuso della sorveglianza della popolazione o di parte di essa, ereditando le idee che fin dalla fine del XVIII° secolo caratterizzarono il progetto del Panopticon di benthamiana memoria, cui nel tempo si sono ispirati più o meno dichiaratamente filosofi, giuristi e scrittori, non da ultimo quel George Orwell che col suo ''1984'' ha impresso per sempre nell’immaginario collettivo il timore del Grande Fratello.

=== #Reclaimyourface ===
L’ansia della sorveglianza di massa non da ieri si è trasferita nei timori dei cittadini; basti ricordare, in questo senso, come già nel 2008 la comunità internazionale della sicurezza informatica e dell’attivismo digitale abbiano sperimentato – con alterne fortune – soluzioni tecnologiche atte a '''limitare''' la possibilità di acquisizione di dati biometrici da parte di sistemi di videosorveglianza sperimentazioni che sono proseguite e proseguono tutt’ora, con una sempre maggiore attenzione per l’argomento, “complice” lo straordinario progresso tecnologico in tema di riconoscimento facciale (e più in generale biometrico) che sta sempre più accrescendo la precisione – e la pervasività – di questi trattamenti di dati personali che, senza dubbio alcuno, ricadono nell’ambito di applicazione del GDPR.

In questo contesto di sempre crescente attenzione alla proliferazione di tecnologie ed installazioni finalizzate alla sorveglianza massiva della popolazione si innesta la recente iniziativa della società civile denominata '''#reclaimyourface''' che si batte affinché che le autorità locali e nazionali ascoltino le loro comunità nella valutazione dei rischi derivanti dall’uso del riconoscimento facciale e di altre tecnologie biometriche negli spazi pubblici. La campagna mira a '''far vietare la sorveglianza biometrica di massa''', in risposta alla diffusione di tecnologie che appaiono estremamente invasive in uno scenario ancora estremamente “fluido” a livello mondiale: se da un lato n Europa, negli Stati Uniti .

Sotto il profilo del trattamento dei dati personali la questione – che come abbiamo visto non è esattamente una “novità dell’ultima ora” ma si trascina da tempo – pare essere entrata in una nuova stagione e l’evoluzione dei sempre più sofisticati algoritmi e sistemi di machine learning e intelligenza artificiale certamente continuerà a portare la problematica sempre più in primo piano.[[CiteRef::gilardiVideosorveglianzaBiometricaEnti2021]]</blockquote>

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Center of Urban Supervision (Roubaix)

2022-05-03T12:31:14Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Facial Recognition (Non-descript)
|Has event={{HasEvent|Start|2020/01/16|Documented}}|City=Roubaix
|Country=France
|managed by=City of Roubaix
|Datasets Used=Live camera feeds
|Software Deployed=Briefcam (Software)
|used by=Police Municipale de Roubaix
}}
[[CiteRef::roubaixxlHotelPoliceMunicipale2020]]
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SNEF Smart CCTVs in Marseille

2022-05-03T12:31:14Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2019/12/01|Documented}}|City=Marseille
|Country=France
|managed by=Mairie de Marseille
|used by=Police Municipale de Marseille
|Software Deployed=Search software from SNEF
}}
== Description ==

[[CiteRef::vitardMarseilleDevantJustice2020]]
[[CiteRef::MarseilleDevantJustice]]

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ViSense at MINDBase

2022-05-03T12:31:14Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2020|Documented}}|City=Rotterdam
|Country=Netherlands
|managed by=Dutch Defence Equipment Organisation
|Involved Entities=TNO
|Software Deployed=ViSense Social Distancing Monitor
|Keywords=COVID-19
}}
<blockquote>
Er is een civiel waarschuwingssysteem in ontwikkeling waarmee onder meer Defensie zich kan wapenen tegen het coronavirus. ViSense geeft een alarm af wanneer mensen geen 1,5 meter afstand houden. De Defensie Materieel Organisatie (DMO) test het snufje momenteel op de MINDBase in Rotterdam.
[[CiteRef::15meteralarm2020]]
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People counting in Nijmegen

2022-05-03T12:31:14Z

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{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Analytics
|Has event={{HasEvent|Start|03/2019|Documented}}|City=Nijmegen
|Country=Netherlands
|managed by=Intemo
|used by=Nijmegen municipality
|Datasets Used=No Dataset Used
|Software Deployed=Numina (Technology)
|Keywords=People counting
|Included in graph=Greens Report
}}
<blockquote>
n maart 2019 gingen twintig sensoren die gebruik maken van deze technologie live. De gemeente Nijmegen is de eerste die werkt met dit type sensoren om passantentelling in kaart te brengen. In plaats van wifitrackers wordt gebruik gemaakt van camera’s. De camera is uitgerust met software waarin de beelden direct worden omgezet naar niet-visuele en niet-persoonlijke gegevens. De bewegingen van mensen, fietsers en auto’s die in beeld komen worden omgezet in lijnen (afbeelding 1). Dit is de enige informatie die in de centrale applicatie binnenkomt. Niemand heeft toegang tot de originele camerabeelden; de ruwe data wordt verwijderd zodra deze verwerkt is. De gemeente kocht de sensoren zelf en betaalt nu enkel nog onderhoudskosten voor het gebruik van de infrastructuur aan de leverancier. Daarnaast is er een verwerkingsovereenkomst opgesteld met het bedrijf dat de sensoren ont-wikkelt en plaatst, waarin staat vastgelegd dat zij geen toegang hebben tot de ruwe data, noch tot de verwerkte data.

[[CiteRef::schoutenVolgJijNog2019]]
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Facial Recognition National Mugshot Database (Sweden)

2022-05-03T12:31:13Z

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2020/11/01|Documented}}|City=Stockholm
|Country=Sweden
|managed by=Swedish Police Authority
|used by={{InstitutionCertainty|Swedish Police Authority|Documented|sirgmetsSummaryReportProject2021}}
|used by={{InstitutionCertainty|Swedish Police Authority|Documented|sirgmetsSummaryReportProject2021}}
|Datasets Used=National Mugshot Database (Sweden)
|Software Deployed=In-house Software National Forensic Centre (Sweden)
}}

[[CiteRef::sirgmetsSummaryReportProject2021]]
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Test of One and a half meter monitor

2022-05-03T12:31:13Z

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|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Surveillance, Crowd management
|Has event={{HasEvent|Start|10/2020|Documented}}|City=Amsterdam
|Country=Netherlands
|managed by=Amsterdam-Amstelland safety region
|Datasets Used=COCO
|Software Deployed=One and a half meter monitor
}}
<blockquote>
Because of COVID19 measures have been taken to prevent person to person detection, one of the most influential and important new regulation is to keep 1.5 meters distance from each other. To help remind citizens to maintain this distance we have created the 1.5 meter monitor.

The 1.5 meter monitor is a system created to make users aware of social distancing measures. It uses a camera, computer with GPU (or edge devices) and a screen to create awareness for anybody within the field of view of the camera to keep social distance. It does so by displaying a picture that is taken of passersby, that is overlayed with a smiley: a green smiley for more than 1.8 meters distance, an orange smiley for 1.5-1.8 meters distance and a red one for less than 1.5 meters distance. An audio signal can also be played.

The system does not store any visual information, and when detected people will be displayed with a smiley overlay to prevent visual recognition. We do record the number of passers-by within range of the camera, to monitor whether it is too busy in a certain area.

We are using the 1.5 meter monitor temporarily because of the COVID-19 Emergency Ordinance and because of the need to keep a distance of 1.5 meters. The system is being tested in public spaces at a number of locations in the Amsterdam-Amstelland Safety Region.
</blockquote>

<blockquote>
This is a low-risk algorithm. None of the images are stored for later use. To measure the effectiveness of the 1.5 meters monitor, qualitative interviews will be conducted.

The chairman of the Amsterdam – Amstelland Safety Region decreed that the 1.5 meters monitor should be linked to the presence of the Emergency Ordinance COVID-19 and / or the necessity of keeping a distance of 1.5 meters in formal legislation. As soon as the Emergency Ordinance COVID-19 is withdrawn and / or keeping a distance of 1.5 meters is no longer necessary on the basis of formal legislation, the legal basis under the 1.5 meters monitor will be removed.
</blockquote>

[[URL::https://algoritmeregister.amsterdam.nl/en/one-and-a-half-meter-monitor/]]
[[CiteRef::cityofamsterdamOneHalfMeter2021]]
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ViSense on the A67

2022-05-03T12:31:13Z

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{{Deployments
|Excluded from graph=Greens Report 2021
|Deployment Status=Ongoing
|Deployment Type=Analytics
|Has event={{HasEvent|Start|2018/09/12|Documented}}|City=Geldrop
|Country=Netherlands
|managed by=ViNotion
|Datasets Used=RDW database
|Keywords=Training
|used by=Rijkswaterstaat
}}
<blockquote>
During the investigation that will last for one year, recordings will be made from passing vehicles on the A67. Using the license plate, information on the vehicle will be obtained from the RDW database, such as the brand and model. The camera images are directly anonimized by rendering the license plate invisible. The license plate information will not be stored so that the images cannot be used to identify persons or vehicles. All data is stored using encryption and the images will not be made public in any way. During the research period, the data will be used to train a traffic analysis system to recognize the appearance of different types of vehicles.

[[CiteRef::vinotionAnnouncementResearch2018]]

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Home Quarantine App Hungary

2022-05-03T12:31:12Z

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Smartphone App
|Has event={{HasEvent|Start|2020/05/01|Documented}}|City=Budapest
|Country=Hungary
|Datasets Used=Facial Image Registry (Hungary)
|Software Deployed=Smartphone App by Asura Technology
}}
[[CiteRef::hazikarantenrendszerHaziKarantenRendszer2021]]

<br />
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Use of Clearview AI in Sweden

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{{Deployments
|Has event={{HasEvent|Start|2019/10/31|Documented}}|City=Stockholm
|Country=Sweden
|managed by=Swedish Police Authority
|used by=Swedish Police Authority
|Involved Entities=Swedish Authority for Privacy Protection (IMY)
|Software Deployed=Clearview AI (Software)
}}
==Description==
<blockquote>'''The Swedish Authority for Privacy Protection finds that the Swedish Police Authority has processed personal data in breach of the Swedish Criminal Data Act when using Clearview AI to identify individuals.'''

Upon news in the media of the Swedish Police Authority using the application [[Clearview AI]] for facial recognition the [[Swedish Authority for Privacy Protection (IMY)]] initiated an investigation against the Police.

The investigation concludes that [[Clearview AI]] has been used by the Police on a number of occasions. According to the Police a few employees have used the application without any prior authorisation.

IMY concludes that the Police has not fulfilled its obligations as a data controller on a number of accounts with regards to the use of Clearview AI. The Police has failed to implement sufficient organisational measures to ensure and be able to demonstrate that the processing of personal data in this case has been carried out in compliance with the Criminal Data Act. When using Clearview AI the Police has unlawfully processed biometric data for facial recognition as well as having failed to conduct a data protection impact assessment which this case of processing would require.

There are clearly defined rules and regulations on how the Police Authority may process personal data, especially for law enforcement purposes. It is the responsibility of the Police to ensure that employees are aware of those rules, says Elena Mazzotti Pallard, legal advisor at IMY.

IMY imposes an administrative fine of SEK 2,500,000 (approximately EUR 250,000) on the Police Authority for infringements of the Criminal Data Act. IMY also orders the Police to conduct further training and education of its employees in order to avoid any future processing of personal data in breach of data protection rules and regulations.

In addition, the Police are ordered to inform the data subjects, whose data has been disclosed to Clearview AI, when confidentiality rules so allows. Finally, the Police are ordered to ensure, to the extent possible, that any personal data transferred to Clearview AI is erased. [[CiteRef::oconnellSwedishDPAPolice2021]]</blockquote>
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Mannheim public surveillance

2022-05-03T12:31:12Z

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Has event={{HasEvent|Start|2018/01/01|Documented}}|City=Mannheim
|Country=Germany
|managed by=Mannheim Police
|Software Deployed=Crowd Management by Fraunhofer Institute Karlsruhe
|Importance=100
}}
68 cameras were installed on central squares and places in the German city Mannheim to record the moving patterns of people. In this project, which started in 2018, the software is used to detect conspicuous behaviour. The police are alerted and investigate the situation further (Reuter 2018). The cameras were placed in areas with increased incidences of criminal activities. Only two minutes lie between the alert of the system and the intervention by the police on average. As the software is learning, it is increasingly able to detect criminal or violent activity. However, sometimes the alerts are not correct, for instance, the system cannot recognize a hug as not dangerous (heise online 2020). The software is developed by the Fraunhofer Institute of Optronics, System Technologies, and Image Exploitation Karlsruhe and is continuously tested and adapted to be suitable for public spaces. Twenty cameras are used to test the software (Ministerium für Inneres 2020).

Reuter, Markus (4.12.2018). Mannheim startet Videoüberwachung mit Verhaltensscannern. Netzpolitik.org. Retrieved on 18 April 2021 from https://netzpolitik.org/2018/mannheim-startet-videoueberwachung-mit-verhaltensscannern/.[[CiteRef::reuterMannheimStartetVideouberwachung2018]]

Heise online (24.8.2020). Polizei Mannheim: Smarte Videoüberwachung hilft – “aber kein Allheilmittel”. Retrieved on 18 April 2021 from https://www.heise.de/news/Polizei-Mannheim-Smarte-Videoueberwachung-hilft-aber-kein-Allheilmittel-4876470.html [[CiteRef::onlinePolizeiMannheimSmarte2020]]

Ministerium für Inneres, Digitalisierung und Migration (15.05.2020). Zwischenergebnisse des Pilotprojekts zur “intelligenten Videoüberwachung” in Mannheim. Drucksache 16/8128. Retrieved 21 April from https://www.landtag-bw.de/files/live/sites/LTBW/files/dokumente/WP16/Drucksachen/8000/16_8128_D.pdf [[CiteRef::ministeriumfurinneresdigitalisierungundmigrationZwischenergebnissePilotprojektsZur2020]]
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IFRS (Interpol)

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{{Deployments
|Information Certainty=Documented
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2017|Documented}}|City=Lyon
|Country=France
|managed by=Interpol
|Potentially used by={{PotentiallyUsedBy|Interpol|Documented|sirgmetsSummaryReportProject2021|p.149-150}}
|Datasets Used=IFRS
|Software Deployed=Unknown AFR Software used by Interpol
}}
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* [[Cited references::References#_SCITE169b5315e2d94f99d6503d35f09e8be2]]
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Deployment of TAJ

2022-05-03T12:31:06Z

Andre: Text replacement - "\|Deployment Start Date=(.*) " to "|Has event={{HasEvent|Start|$1|Documented}}"

{{Deployments
|Deployment Status=Ongoing
|Deployment Type=Criminal investigations
|Has event={{HasEvent|Start|2013|Documented}}|City=Paris
|Country=France
|managed by=Ministry of Interior (France)
|used by=Gendarmerie Nationale, Police Nationale, Ministry of Interior (France)
|Datasets Used=TAJ
|Software Deployed=Cognitec Face VACS DBScan
|Information Certainty=Academic Report
}}
[[CiteRef::sirgmetsSummaryReportProject2021]]
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Smart Sporting Cities in Leuven

2022-05-03T12:26:18Z

Andre:

{{Deployments
|Excluded from graph=Greens Report 2021
|Deployment Status=Ongoing
|Deployment Type=Sports Analytics
|Has event={{HasEvent|Start|2021/08/01|Documented}}{{HasEvent|End|2024/08/01|Documented}}
|City=Leuven
|Country=Belgium
|managed by=City of Leuven
|used by=City of Leuven, Sport Flanderen
|Involved Entities=Programma Innovatieve Overheidsopdrachten (PIO), Vlaams Departement Economie, Wetenschap en Innovatie
}}

=== Municipality of Leuven Page [[CiteRef::stadleuvenMappingSportsExercise2021]] ===
<blockquote>
==== Sport- en beweeggedrag in kaart brengen met slimme technologie ====
Met het project 'Smart Sporting Cities' meten we met slimme technologie het sport- en beweeggedrag van Leuvenaars in de publieke ruimte.

==== Wat en hoe meten we? ====
We richten 2 proeftuinen in: het Bruulpark en de Philipssite. Op beide locaties ontdekken we de komende 3 jaar (2021-2024) met slimme camera’s hoeveel mensen er sporten, hoe vaak, wanneer en welke beweegactiviteiten ze er beoefenen.

==== Park De Bruul ====
In de eerste proeftuin in Park De Bruul monitoren we specifiek de sportinfrastructuur van het park.

Het groene park in het centrum van Leuven heeft een multifunctioneel grasveld, een voetbalkooi en een sportmuur voor tennis en urban fitness.

De technologie krijgt er een vaste plaats tot de zomer van 2024, om het sportgedrag continu te monitoren.

==== Philipssite ====
De tweede proeftuin bevindt zich op de Philipssite, aan de ring van de stad.

Vanaf 1 oktober 2021 (na het WK wielrennen) testen we daar mobiele technologieën. Met deze mobiele toepassingen kan je de meetapparatuur na een bepaalde periode verplaatsen naar andere sites. Deze toepassingen zijn dankzij een zonnepaneel en een 4G/5G-verbinding onafhankelijk van stroom- en datavoorzieningen in de publieke ruimte.

Op de Philipssite focussen we op het multifunctionele grasveld en het barpark tussen sportcentrum Sportoase en de Leuvense ring.

==== Data ====
Op beide locaties verzamelen we deze data:

* Hoeveel sporters en bewegers gebruiken de publieke ruimte?
* Hoelang gebruiken sporters en bewegers de publieke ruimte?
* Hoeveel sporters en bewegers gebruiken een beweegroute zoals een fietspad of looproute?
* Leggen de sporters en bewegers een beweegroute alleen af of in groep?
* Hoe snel bewegen sporters en bewegers op een beweegroute?
* Hoeveel sporters en bewegers gebruiken de sportinfrastructuur en de recreatievoorzieningen en wanneer?
* Welke beweegactiviteiten oefenen sporters en bewegers uit?

==== Wat doen we met de resultaten? ====

* Met de data kunnen we sporten en bewegen op een heel lokale schaal '''in kaart brengen'''.
* We kunnen '''evalueren''' of acties het juiste effect hebben. Zo kunnen we een beter sport- en beweegbeleid uitstippelen voor de toekomst.
* Sport Vlaanderen ontwikkelt op basis van de resultaten een '''draaiboek''' over het gebruik en de waarde van '''Internet Of Things (IoT) toepassingen''' om sport en beweging te meten. Met het draaiboek en de IoT-toepassingen kunnen andere steden en gemeenten in de toekomst zelf gerichte meetcampagnes over beweging opzetten.

==== We respecteren jouw privacy ====
De opdracht ‘Smart Sporting Cities’ werd gegund aan een consortium van bedrijven, getrokken door Cronos Public Services. Zij plaatsten in de 2 proeftuinen camera’s, maximaal rekening houdend met de dataveiligheid en de bescherming van persoonsgegevens.

Zo worden de beelden uit de proeftuinen ‘on edge’ of op de camera verwerkt via artificiële intelligentie en worden alleen telgegevens of kenmerken van bepaalde parameters (individueel bezoek of in groep, type van sportactiviteit, tijdsduur van het bezoek aan de proeftuin …) doorgestuurd naar de databank.

Sport Vlaanderen en stad Leuven voerden samen ook een gegevensbeschermingseffectenbeoordeling uit om de datarisico’s op te lijsten en de nodige veiligheidsmaatregelen te nemen.

==== Timing ====
Het project loopt van augustus 2021 tot de zomer van 2024.

Het project is een samenwerking tussen stad Leuven en Sport Vlaanderen. Het wordt gerealiseerd met de ondersteuning van het Programma Innovatieve Overheidsopdrachten (PIO) van het Vlaams Departement Economie, Wetenschap en Innovatie.

==== Smart City Leuven ====
Stadskantoor

Professor Van Overstraetenplein 1

3000 Leuven

* '''Tel.'''[[Tel:016272371|016 27 23 71]]
* '''E-mail'''smartcity@leuven.be
</blockquote>
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Smart CCTV with audio detection in Rouen public transportation

2022-05-03T12:26:08Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|07/2020|Documented}}{{HasEvent|End|07/2023|Documented}}
|City=Rouen
|Country=France
|used by=City of Rouen
|Software Deployed=Unknown Audio/Video Analytics Software (Rouen)
|Keywords=Vidéosurveillance automatisée, Capteurs audios, Technologies, innovation, R&D, Transport
|Managed by=Métropole de Rouen
}}
<blockquote>Dispositif de caméras intelligente avec visualisation en temps réel au CSU. Ainsi que des "capteurs d'incivilité" c'est à dire des écoutes sonores. 20 bus sont équipés de 5 caméras (4 à l'intérieur et une à l'extérieur). [[CiteRef::technopoliceFrMarch2020]]</blockquote>

=== Additional properties ===

* Budget [[Budget::250000000000]]
* CiteRef [[CiteRef::technopoliceFrMarch2020]]
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Project VOIE (Vidéoprotection Ouverte et IntégréE)

2022-05-03T12:25:53Z

Andre:

{{Deployments
|Excluded from graph=Greens Report 2021
|Deployment Status=Concluded
|Has event={{HasEvent|Start|06/2016|Documented}}{{HasEvent|End|01/2019|Documented}}
|City=Paris
|Country=France
|used by=Police Nationale, RATP, SNCF
|Software Deployed=VOIE Project Technology
|Keywords=Vidéosurveillance automatisée, Technologies, innovation, R&D, Police et justice
|Managed by=Préfecture de police de Paris, SNCF, RATP, INRIA
}}
<blockquote>Le projet VOIE (« Vidéoprotection Ouverte et IntégréE ») associait des industriels (Thales, Morpho devenu Idemia, Deveryware), des transporteurs (SNCF, RATP), la préfecture de police de Paris et avait bénéficié d’un financement de la Banque publique d’investissement. L’objectif était le suivi d’individus et l’analyse de vidéo dans le cadre de réquisitions judiciaires. Le projet avait même reçu le prix « Coup de Cœur » aux Trophées de la Sécurité 2015. Mais la CNIL s’était opposée aux traitements en temps réel dans l’espace public. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

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Urban Surveillance Center in Marseille

2022-05-03T12:25:36Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|11/2018|Documented}}{{HasEvent|End|01/1970|Documented}}
|City=Marseille
|Country=France
|managed by=City of Marseille
|Software Deployed=Urban Surveillance Platform by SNEF
|Keywords=Vidéosurveillance automatisée, Capteurs audios, Police et justice
|Managed by=Marseille
}}
<blockquote>Le CSU phocéen a déployé fin 2019 un nouveau dispositif de vidéosurveillance intelligente, d'abord sur une cinquantaine de caméras. Comme le relève le cahier des charges du projet, « leur nombre est aujourd’hui très important », rendant l’assistance logicielle “impérative” pour les opérateurs. Grâce à cette béquille informatique, les fonctionnaires pourront repérer un objet abandonné, identifier automatiquement une rixe ou suivre le déroulement d’une manifestation, y compris en captant le son alentours. Interrogée, la CNIL n’aurait jamais entendu parler du projet. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

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Facial Recognition Pilot in High School (Nice)

2022-05-03T12:25:27Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|12/2018|Documented}}{{HasEvent|End|08/2020|Documented}}
|City=Nice
|Country=France
|Software Deployed=Unknown Facial Recognition Software by Cisco
|Keywords=Vidéosurveillance automatisée, Identification biométrique, profilage, Éducation
|Managed by=Région Sud
}}
<blockquote>En décembre 2018, le conseil régional de la région Sud (ex-PACA) a voté une délibération visant à faire installer à l’entrée de deux lycées de la région — le lycée des Eucalyptus à Nice et le lycée Ampère à Marseille — un dispositif de reconnaissance faciale. Ce dispositif expérimental, installé et financé par la société états-unienne Cisco, a vocation, comme l’a précisé M. Christian Estrosi lors du vote au Conseil régional, à être étendu à l’ensemble des établissements scolaires de la région. En dépit du recours déposé en février 2019 par La Quadrature du Net, la LDH, CGT Educ’Action des Alpes-Maritimes et la Fédération des Conseils de Parents d’Élèves des écoles publiques des Alpes-Maritimes, l'installation des portiques de reconnaissance faciale à l'entrée des deux établissements est programmée pour septembre 2019. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

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Facial Recognition Pilot in High School (Marseille)

2022-05-03T12:25:20Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|12/2018|Documented}}{{HasEvent|End|08/2020|Documented}}
|City=Nice
|Software Deployed=Unknown Facial Recognition Software by Cisco
|Managed by=Région Sud
}}
<blockquote>En décembre 2018, le conseil régional de la région Sud (ex-PACA) a voté une délibération visant à faire installer à l’entrée de deux lycées de la région — le lycée des Eucalyptus à Nice et le lycée Ampère à Marseille — un dispositif de reconnaissance faciale. Ce dispositif expérimental, installé et financé par la société états-unienne Cisco, a vocation, comme l’a précisé M. Christian Estrosi lors du vote au Conseil régional, à être étendu à l’ensemble des établissements scolaires de la région. En dépit du recours déposé en février 2019 par La Quadrature du Net, la LDH, CGT Educ’Action des Alpes-Maritimes et la Fédération des Conseils de Parents d’Élèves des écoles publiques des Alpes-Maritimes, l'installation des portiques de reconnaissance faciale à l'entrée des deux établissements est programmée pour septembre 2019. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

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Facial Recognition in Brussels Airport (Stopped)

2022-05-03T12:25:09Z

Andre:

{{Deployments
|Deployment Status=Stopped
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2019-07-01|Documented}}{{HasEvent|End|2019-09-20|Documented}}
|City=Zaventem
|Country=Belgium
|managed by=Belgian Federal Police
|used by=Belgian Federal Police
|Involved Entities=Belgian Data Protection Authority (GBA)
|Datasets Used=Live camera feeds
|Software Deployed=Unknown_Technology_0006
|Importance=100
}}
==Description==
<blockquote>Il s’agit d’une information rapportée vendredi par De Standaard et confirmée par l’Organe de contrôle de l’information policière (COC).

Marc De Mesmaeker avait annoncé en juillet dernier dans le magazine Knack que des caméras à reconnaissance faciale automatique seraient mises en place à l’aéroport. Une modification législative pour ce faire n’était, selon lui, pas nécessaire.

Le COC voyait l’initiative d’un autre oeil et a ouvert une enquête. Entre-temps, l’organe de contrôle a ordonné que le projet soit provisoirement abandonné. «Et la police fédérale a confirmé que c’était le cas», a indiqué Frank Schuermans, conseiller au COC. L’instance a établi que le projet contrevenait à la loi sur la fonction de police et celle sur la protection des données. «La surveillance par caméra reste possible, mais la reconnaissance faciale ne l’est pas», a précisé le conseiller.

La police fédérale a expliqué au journal flamand être déjà en train de se pencher sur l’avis du COC avec les partenaires impliqués dans le projet. La police ambitionne d’ailleurs toujours d’utiliser la reconnaissance faciale.[[CiteRef::sudinfo.bePoliceFederaleTestait2019]]</blockquote>

==Interruption of the Zavantem Pilot Project==
<blockquote>As of February 2020, FR has not been implemented in Belgium in relation to criminal investigations. The principal reason for this is the legal restrictions that exist within the country. There is legislation in place that allows the police to use intelligent cameras for law enforcement purposes but, it is not permissible to keep track of the captured images (even for a few seconds). [[CiteRef::sirgmetsSummaryReportProject2021]]

After the Brussels terrorist bomb attacks in March 2016, a pilot project using FR was started at Brussels Airport but, concerns were expressed by the Belgian Data Protection Authority (GBA) about the legality of these activities and this led to the work being stopped. [[CiteRef::sirgmetsSummaryReportProject2021]]

</blockquote>

==Possible use of Clearview AI==
<blockquote>The tests at Brussels Airport – as well as recent reporting on possible use by police officers of Clearview AI – shows that clear legal guidelines are needed. As the European Data Protection Board (EDPB) rightfully states in its recent guidelines on processing of personal data through video devices, the use of biometric data – facial recognition in particular – entails heightened risks for data subjects’ rights. Certainly, both police forces and citizens would benefit from legal clarity. It would appear from the Commission white paper on AI that the Commission shares this view, as they allude on further regulation of systems for facial recognition.[[CiteRef::peetersFacialRecognitionBrussels2020]]</blockquote>

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Korte Putstraat (Stopped)

2022-05-03T12:25:00Z

Andre:

{{Deployments
|Deployment Status=Stopped
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2019/03/02|Documented}}{{HasEvent|End|2019/03/05|Documented}}
|City=Den Bosch
|Country=Netherlands
|managed by=CrowdWatch
|Datasets Used=Local Watchlist
|Software Deployed=Unknown Live AFR in Korte Putstraat
}}
<blockquote>
[[Number of Individuals Detected::11381|11.381 gezichten]] hebben de slimme camera's op de Korte Putstraat dinsdag tegen vieren al herkend, en de teller loopt op. Want niemand komt dit jaar meer ongezien de smalle carnavalsstraat in, ook niet met plaksnor of kek brilletje op.
...
En het systeem werkt, geeft ook hij aan. Al was het qua incidenten wel een rustige carnaval, vertelt Boujrad. Het aantal mensen dat daadwerkelijk een verbod krijgt voor de straat is ‘op twee handen te tellen’. ,,Alleen mensen die zich echt misdragen krijgen een straatverbod voor drie dagen. We geven bezoekers die dan straat uit worden gezet ook direct de boodschap mee dat ze herkend worden als ze de volgende keer binnenwandelen. Meestal is de boodschap dan ook wel duidelijk. Slechts [[Number of Individuals Recognised::2|twee keer]] hebben de camera’s iemand herkend die alsnog naar binnen wilde. Die hebben we er dan zo uitgepikt.” Angst voor aantasting van de privacy is in elk geval niet nodig, vertelt Adema. ,,De software kijkt alleen naar mensen die al iets misdaan hebben. Alle andere herkende gezichten worden weer verwijderd.”
[[CiteRef::gotinkSlimmeCameraHerkennen2019]]
</blockquote>
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Safe City Pilot (La Défense)

2022-05-03T12:24:51Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|01/2018|Documented}}{{HasEvent|End|01/2020|Documented}}
|City=La Défense
|Country=France
|Software Deployed=Safe City Solution by Thalès
|Managed by=Région Île-de-France, Département des Hauts-de-Seine, Grand Paris
}}
=== Additional properties ===
* Geolocation [[Geolocation::48.89093739792309°,2.242435148776779°]]
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* [[Cited references::References#_SCITEe472d97ffb92f6cd3330130404b9e8ed]]
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Facial Recognition Pilot Project during Carnival (Nice)

2022-05-03T12:24:41Z

Andre:

{{Deployments
|Information Certainty=Documented
|Deployment Status=Concluded
|Deployment Type=Surveillance
|Has event={{HasEvent|Start|2019/02/16|Documented}}{{HasEvent|End|2019/03/02|Documented}}
|City=Nice
|Country=France
|managed by=City of Nice
|used by=Nice Municipal Police
|Datasets Used=Ad-hoc Simulation Dataset
|Software Deployed=AnyVision Software
|Managed by=Nice
}}
<blockquote>Les 16, 19 et 20 février 2018, la ville de Nice a expérimenté l'usage de systèmes de reconnaissance faciale pour des personnes consentantes venues assister au carnaval de la ville. L'expérimentation, fondée sur le consentement de personnes volontaires (avec une possibilité pour les personnes ne voulant pas participer à l'expérimentation de passer par un endroit où le dispositif n'est pas installé), a été réalisée avec l'aide de 6 caméras de vidéosurveillance, relié au logiciel d'analyse Any Vision, développé par une start-up monégasque appelée Confidentia.
Selon la presse, le test réalisé le samedi 16 février sur cinq personnes qu'il s'agissait de retrouver au sein de la foule passant par le dispositif. Pour une de ces personnes, la photo enregistrée dans la base de données datait de 30 ans. Le test aurait été positif pour ces cinq personnes. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

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Face mask recognition in Châtelet-Les Halles (Stopped)

2022-05-03T12:24:28Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|2020/05/06|Documented}}{{HasEvent|End|2020/06/12|Documented}}
|City=Paris
|Country=France
|used by=RATP
|Involved Entities=CNIL
|Datasets Used=None
|Software Deployed=Mask Detection by Datakalab
|Managed by=RATP
}}
===Additional properties===

*Geolocation [[Geolocation::48.857044033090624°,2.353808393826637°]]

<blockquote>La RATP a coupé court à une expérimentation de suivi du port du masque, lancée dans le métro parisien de Châtelet-les-Halles. Les voyageurs n'avaient aucune possibilité de signifier leur refus.

Elles étaient actives depuis le 6 mai, et devaient l'être pour trois mois. Les caméras "intelligentes" déployées dans le métro parisien, et plus précisément dans la station de Châtelet-les-Halles, pour suivre et mesurer le port du masque ont été mises en pause pour une durée indéterminée, fait savoir la RATP.

Un coup d'arrêt a été porté à cette expérimentation, qui permettait jusqu'à présent d'évaluer dans quelle mesure les usagers du métro se pliaient aux consignes sanitaires. Ces mêmes usagers étaient en effet seulement prévenus de l'existence de ce test, sans pouvoir s'y soustraire. Six caméras avaient dans un premier temps été activées et douze devaient l'être à terme. Toutes étaient équipées d'un détecteur de masques, conçu par la start-up française Datakalab. Le suivi réalisé permettait d'obtenir, en temps réel, le nombre exact et le pourcentage de personnes masquées, sans finalité de verbalisation.

"Depuis le 12 juin, la RATP a décidé de suspendre temporairement l’expérimentation sur la détection du port du masque à Châtelet, afin de compléter le dispositif de droit d’opposition tel que demandé par la CNIL", note le service de transports auprès de BFM Tech.
</blockquote>

====Pas de droit d'opposition====
<blockquote>Le 17 juin, par un avis rendu public, la Commission nationale de l’informatique et des libertés (Cnil) a rappelé le droit de chacun à s'opposer à la captation de son image dans l'espace public, en vertu du règlement général sur la protection des données (RGPD) en vigueur dans l’Union européenne. L'institution s'est montrée particulièrement précautionneuse au sujet des caméras "intelligentes", telles qu'utilisées dans la station de Châtelet-les-Halles.

"Si les objectifs de ces dispositifs censés contribuer à la lutte contre la propagation du coronavirus sont “le plus souvent légitimes (...), leur développement incontrôlé présente le risque de généraliser un sentiment de surveillance chez les citoyens, de créer un phénomène d’accoutumance et de banalisation de technologies intrusives, et d’engendrer une surveillance accrue”, écrit-elle dans son communiqué, en soulignant le risque de "porter atteinte au bon fonctionnement de notre société démocratique".

Ces dispositifs, poursuit la CNIL, ne doivent par ailleurs être déployés qu’avec le consentement des personnes filmées ou s’ils sont “nécessaires” aux objectifs poursuivis, avec l'impératif de répondre au principe de “proportionnalité”, et donc de ne pas porter une atteinte disproportionnée à la vie privée.
</blockquote>

====Déconfinement: des caméras détecteront le port du masque à la station Châtelet====
<blockquote>Contactée par BFM Tech, la société Datakalab n'indique pas encore de quelle manière ses caméras pourront s'assurer du consentement des usagers de la RATP, ni même quand l'expérimentation pourrait reprendre. Dans trois marchés de Cannes, ville avec laquelle Datakalab avait également noué un partenariat, les passants étaient invités à manifester leur refus de participer à une expérimentation similaire, d'un simple signe "non" de la tête. La solution avait été jugée impraticable par la Cnil. La mairie de Cannes indique que cette expérimentation a, elle aussi, été suspendue. Elle avait néanmoins vocation à accompagner le déconfinement, et non à se prolonger indéfiniment.
[[CiteRef::bfmtvTropIntrusivesCameras2020]]</blockquote>
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S²UCRE ANR (Research Project)

2022-05-03T12:24:13Z

Andre:

{{Deployments
|Excluded from graph=Greens Report 2021
|Deployment Status=Planned
|Deployment Type=Crowd management
|Has event={{HasEvent|Start|06/2019|Documented}}{{HasEvent|End|06/2020|Documented}}
|City=Paris
|Country=France
|managed by=ANR
|Software Deployed=S²UCRE Technology Package
|Managed by=Préfecture de police de Paris, INRIA
}}
<blockquote>S²UCRE : S²UCRE (« Safety and Security of UrbanCrowded Environments »). Mené avec l’Allemagne et bénéficiant d’un financement d’un million d’euros de l’Agence nationale pour la recherche, S²UCRE est destiné à la gestion des foules et vise à « combler le gap entre le niveau macro de la surveillance d’une foule et l’observation micro d’individus ou de groupes ». Il combine cinq domaines technologiques, basés sur de l’analyse vidéo avec des méthodes de simulation : outre la surveillance d’une foule, l’objectif est de prédire son comportement à court terme, de repérer des comportements suspects, détecter et géolocaliser des auteurs d’infraction et repérer les équipes de sécurité.

Pour expérimenter et passer outre les barrières réglementaires françaises, les autorités françaises ont passé un partenariat avec Singapour, modèle mondial de Safe city, qui teste cette solution. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

=== Additional properties ===

* Budget [[Budget::1000000]]
* URL [[URL::Page du projet sur le site de l'ANR|https://anr.fr/Projet-ANR-16-SEBM-0001]]
* Geolocation [[Geolocation::48.8566969°,2.3514616°]]
* CiteRef [[CiteRef::technopoliceFrMarch2020]]
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Smart video surveillance in Valenciennes

2022-05-03T12:24:00Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|11/2016|Documented}}{{HasEvent|End|01/1970|Documented}}
|City=Valenciennes
|Country=France
|managed by=City of Valenciennes
|Software Deployed=Safe City Software by Huawei
|Keywords=Vidéosurveillance automatisée, Police et justice
|Managed by=Ville de Valenciennes
}}
<blockquote>En 2017, la ville de Valenciennes a passé un contrat avec l'entreprise chinoise Huawei pour devenir « la première ville de la région à bénéficier de l’expertise technologique de Huawei dans le cadre du renouvellement de son parc de vidéoprotection ». Le maire de Valenciennes veut ainsi faire de sa ville « une vitrine technologique, sûre et de confiance, au service des valenciennois ».

Le projet consiste en le dépoiement de 217 caméras dites de nouvelle génération (dont un traitement intelligent de l'image avec détection de mouvements de foule et de « situations inhabituelles » et d'un centre de surveillance renouvellé.

Sur le site de valenciennes.fr, il est par ailleurs indiqué que « Après une première phase d’expérimentation via l’installation de caméras sur le secteur de la gare, les 160 caméras du parc communal ont été remplacées de janvier à juin par de nouvelles caméras de haute performance. Pour compléter ce dispositif, 70 caméras supplémentaires vont être installées d’ici à fin juin 2019, soit un total de 230 caméras sur la ville ».

Le contrat est un partenariat public-privé. Dans les documents obtenus à la suite d'une demande CADA, il est dit que le matériel, c'est à dire les caméras, le serveur et le logiciel sont délivrés « à titre gracieux » par la société pour la durée de l'expérimentation.

Néanmoins, il est inscrit dans le contrat que la maintenant est assurée par Huawei pour un coût total de 24.000 euros (pour une duré de 3 ans).. [[CiteRef::technopoliceFrMarch2020]]</blockquote>

=== Additional properties ===
* Geolocation [[Geolocation::50.35°,3.53333°]]
* CiteRef [[CiteRef::technopoliceFrMarch2020]]
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* [[Cited references::References#_SCITEe472d97ffb92f6cd3330130404b9e8ed]]
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Safe City Pilot Project (Nice)

2022-05-03T12:23:50Z

Andre:

{{Deployments
|Has event={{HasEvent|Start|02/2018|Documented}}{{HasEvent|End|02/2021|Documented}}
|City=Nice
|Country=France
|Software Deployed=Safe City Solution by Thalès
|Managed by=Nice, Métropole Nice Côte d’Azur
|Importance=100
}}
<blockquote>À Nice, dans le cadre de ce nouveau « Projet d’expérimentation Safe City » a été voté par la Ville en juin, Thales et le consortium qu’il dirige veulent aller plus loin, en créant le « Waze de la sécurité » à la mode Big Data. Pour aider les « décideurs », il s’agit de « collecter le maximum de données existantes et d’en chercher les corrélations et les signaux faibles », de « développer les nouveaux algorithmes d’analyse et de corrélation permettant de mieux comprendre une situation et de développer des capacités prédictives ». La surveillance des réseaux sociaux et autres « données ouvertes » sont en ligne de mire, notamment pour les « textes courts et mal écrits » chers à Twitter qui passeront à la moulinette d’outils d’analyse sémantique pour aider à la gestion des foules, à l’analyse des rumeurs, et le « suivi des actions de certains individus ou groupes de personnes ». [[CiteRef::technopoliceFrMarch2020]]</blockquote>

=== Additional properties ===

* Geolocation [[Geolocation::43.71017075267187°,7.2548493848708°]]
* CiteRef [[CiteRef::technopoliceFrMarch2020]]

[[TestRef::References#_SCITEe472d97ffb92f6cd3330130404b9e8ed]]
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* [[Cited references::References#_SCITEe472d97ffb92f6cd3330130404b9e8ed]]
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