10.5281/zenodo.5865875
https://zenodo.org/records/5865875
oai:zenodo.org:5865875
Rannou, Emilie
Emilie
Rannou
Ekimetrics
Benichoux, Alexis
Alexis
Benichoux
Yubo
Forgeas, Rémi
Rémi
Forgeas
France Business Services Center
Gaillard, Simon
Simon
Gaillard
New York
Mary, Jérémie
Jérémie
Mary
Université de Lille
Trinh, Minh
Minh
Trinh
New York
TURINICI, Gabriel
Gabriel
TURINICI
0000-0003-2713-006X
Universite Paris Dauphine - PSL
Waxin, Emilie
Emilie
Waxin
WE Avocats
Deepfakes & Algorithms: Threat or Opportunity?
Zenodo
2021
artificial intelligence
deep learning
generative adversarial networks
GAN
variational auto-encoders
VAE
fake news
algorithms
neural networks
deep fakes
2021-10-01
eng
10.5281/zenodo.5865874
https://zenodo.org/communities/ai_ml
1
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
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 & 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