October 1, 2021 Report Open Access

Rannou, Emilie; Benichoux, Alexis; Forgeas, Rémi; Gaillard, Simon; Mary, Jérémie; Trinh, Minh; TURINICI, Gabriel; Waxin, Emilie

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  <identifier identifierType="DOI">10.5281/zenodo.5865875</identifier>
  <creators>
    <creator>
      <creatorName>Rannou, Emilie</creatorName>
      <givenName>Emilie</givenName>
      <familyName>Rannou</familyName>
      <affiliation>Ekimetrics</affiliation>
    </creator>
    <creator>
      <creatorName>Benichoux, Alexis</creatorName>
      <givenName>Alexis</givenName>
      <familyName>Benichoux</familyName>
      <affiliation>Yubo</affiliation>
    </creator>
    <creator>
      <creatorName>Forgeas, Rémi</creatorName>
      <givenName>Rémi</givenName>
      <familyName>Forgeas</familyName>
      <affiliation>France Business Services Center</affiliation>
    </creator>
    <creator>
      <creatorName>Gaillard, Simon</creatorName>
      <givenName>Simon</givenName>
      <familyName>Gaillard</familyName>
      <affiliation>New York</affiliation>
    </creator>
    <creator>
      <creatorName>Mary, Jérémie</creatorName>
      <givenName>Jérémie</givenName>
      <familyName>Mary</familyName>
      <affiliation>Université de Lille</affiliation>
    </creator>
    <creator>
      <creatorName>Trinh, Minh</creatorName>
      <givenName>Minh</givenName>
      <familyName>Trinh</familyName>
      <affiliation>New York</affiliation>
    </creator>
    <creator>
      <creatorName>TURINICI, Gabriel</creatorName>
      <givenName>Gabriel</givenName>
      <familyName>TURINICI</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2713-006X</nameIdentifier>
      <affiliation>Universite Paris Dauphine - PSL</affiliation>
    </creator>
    <creator>
      <creatorName>Waxin, Emilie</creatorName>
      <givenName>Emilie</givenName>
      <familyName>Waxin</familyName>
      <affiliation>WE Avocats</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Deepfakes &amp; Algorithms: Threat or Opportunity?</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>artificial intelligence</subject>
    <subject>deep learning</subject>
    <subject>generative adversarial networks</subject>
    <subject>GAN</subject>
    <subject>variational auto-encoders</subject>
    <subject>VAE</subject>
    <subject>fake news</subject>
    <subject>algorithms</subject>
    <subject>neural networks</subject>
    <subject>deep fakes</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-10-01</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Report"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5865875</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.5865874</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ai_ml</relatedIdentifier>
  </relatedIdentifiers>
  <version>1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode">Creative Commons Attribution Non Commercial No Derivatives 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Nowadays, deepfakes appear to be a manipulation tool whose impact on society is still poorly understood. Their existence and use raise many legal and ethical questions. Despite the laws and governance rules that may be implemented in response, the inability to detect a deepfake remains a fundamental concern. As technology continues evolving, it becomes more and more complicated to identify a fake. Developing a European knowledge on tools for detecting fakes and authenticating originals appears urgent. To answer this challenge, the latest Praxis report, Deepfakes &amp;amp; Algorithms, makes twelve recommendations around four major strategic pillars:&lt;br&gt;
- Making Europe a leader in the fight against deepfakes&lt;br&gt;
- Strengthening the responsibility of platforms at the European level&lt;br&gt;
- Building a regulatory environment adapted to an efficient fight against deepfakes&lt;br&gt;
- Protecting citizens from the impact of deepfakes&lt;/p&gt;</description>
  </descriptions>
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