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Deepfake Video Generation and Detection

Date : 23-06-2022
Lieu : Paris

Thèmes scientifiques :
  • D - Télécommunications : compression, protection, transmission

Nous vous rappelons que, afin de garantir l'accès de tous les inscrits aux salles de réunion, l'inscription aux réunions est gratuite mais obligatoire.

S'inscrire à la réunion.


24 personnes membres du GdR ISIS, et 6 personnes non membres du GdR, sont inscrits à cette réunion.

Capacité de la salle : 100 personnes.


The word deepfake comes from "deep learning" and "fake". Roughly, it refers to a video, an image or an audio recording in which the protagonist's identity or lyrics have been modified to mimic the protagonist's identity or lyrics from a source video, or even completely generated from scratch. Recent advances in deep learning, including variational auto-encoders and generative adversarial networks (GAN), have enabled the generation of high-quality fake videos and audio. This situation raises many security issues in society, especially when celebrities and politicians are targeted, and their fake videos are widely shared on social networks. Many deepfake detection solutions relying on both handcrafted and deep learning techniques have been proposed in the literature. For instance, some deepfake detection methods rely on visual artifacts, physiological inconsistencies of features space where fake and real videos are well characterized. The race between deepfake generation and detection is still in play to develop more efficient generation and detection solutions.

The aim of this workshop is to bring together industry researchers as well as academics working on deepfake generation and detection from different communities including machine learning, computer vision, image processing, multimedia security and biometric.

Topics of interest in this workshop include the following topics (but are not limited to):

  • Generation of DeepFakes and face manipulation.
  • Generation of synthetic faces including GAN based solutions.
  • Detection of DeepFakes with hand-crafted solutions and deep learning-based solutions.
  • Construction methodology of Deepfake datasets and detection benchmark.
  • Resilience of Deepfake detection solution against adversarial attacks.


  • Wassim Hamidouche (INSA Rennes, IETR), wassim.hamidouche@insa-rennes.fr
  • Amine Kacete (b<>com Insitute, Rennes), amine.kacete@b-com.com
  • Abdenour Hadid (Université de Valencienne), abdenour.hadid@ieee.org

We invite Ph. D. students and researchers to present their work in this workshop, a title and short abstract of their contribution should be sent to organizers before May 27th 2022.