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Deepfakes & Algorithms: Threat or Opportunity?

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

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Rannou, Emilie</dc:creator>
  <dc:creator>Benichoux, Alexis</dc:creator>
  <dc:creator>Forgeas, Rémi</dc:creator>
  <dc:creator>Gaillard, Simon</dc:creator>
  <dc:creator>Mary, Jérémie</dc:creator>
  <dc:creator>Trinh, Minh</dc:creator>
  <dc:creator>TURINICI, Gabriel</dc:creator>
  <dc:creator>Waxin, Emilie</dc:creator>
  <dc:description>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; Algorithms, makes twelve recommendations around four major strategic pillars:
- Making Europe a leader in the fight against deepfakes
- Strengthening the responsibility of platforms at the European level
- Building a regulatory environment adapted to an efficient fight against deepfakes
- Protecting citizens from the impact of deepfakes</dc:description>
  <dc:subject>artificial intelligence</dc:subject>
  <dc:subject>deep learning</dc:subject>
  <dc:subject>generative adversarial networks</dc:subject>
  <dc:subject>variational auto-encoders</dc:subject>
  <dc:subject>fake news</dc:subject>
  <dc:subject>neural networks</dc:subject>
  <dc:subject>deep fakes</dc:subject>
  <dc:title>Deepfakes &amp; Algorithms: Threat or Opportunity?</dc:title>
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