While there are interesting and creative applications of deepfakes, they are also likely to be weaponized. We were among the early responders to this phenomenon, and developed the first deepfake detection method based on the lack of realistic eye-blinking in the early generations of deepfake videos in early 2018. Subsequently, there is a surge of interest in developing deepfake detection methods.
A climax of these efforts is this year’s Deepfake Detection Challenge. Overall, the winning solutions are a tour de force of advanced DNNs (an average precision of 82.56 percent by the top performer). These provide us effective tools to expose deepfakes that are automated and mass-produced by AI algorithms. However, we need to be cautious in reading these results. Although the organizers have made their best effort to simulate situations where deepfake videos are deployed in real life, there is still a significant discrepancy between the performance on the evaluation data set and a more real data set; when tested on unseen videos, the top performer’s accuracy reduced to 65.18 percent. TO READ ENTIRE ARTICLE BY SIWEI LYU
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