Deepfake Detection Challenge Results: An Open Initiative to Advance AI

Facebook has partnered with other industry leaders and academic experts last year to create the Deepfake Detection Challenge (DFDC) in order to accelerate development of new ways to detect deepfake videos. By creating and sharing a unique new data set of more than 100,000 videos, the DFDC has enabled experts from around the world to come together, benchmark their deepfake detection models, try new approaches, and learn from each others’ work. This open, collaborative effort will help the industry and society at large meet the challenge presented by deepfake technology and help everyone better assess the legitimacy of content they see online. As with our recently launched Hateful Memes Challenge, we believe challenges and shared data sets are key to faster progress in AI.

The DFDC launched last December, and 2,114 participants submitted more than 35,000 models to the competition. Now that the challenge has concluded, we are sharing details on the results and working with the winners to help them release code for the top-performing detection models. Next week, at the conference on Computer Vision and Pattern Recognition (CVPR), we will also share details on our plans to open-source the raw data set used to construct DFDC, featuring more than 3,500 actors and 38.5 days’ worth of data. This will help AI researchers develop new generation and detection methods to advance the state of the art in this field. Moreover, this data set will be opened for use for other research work in AI domains as well as work on deepfakes.

To create this challenge, we built a new data set with a wide variety of high-quality videos created expressly for research on deepfakes. DFDC participants used this data set to train and test their models and were able to assess their performance on a public leaderboard on the challenge website. The videos feature more than 3,500 different paid actors, each of whom agreed to participate in the project. We focused in particular on ensuring diversity in gender, skin tone, ethnicity, age, and other characteristics.

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