Ensemble Learning Model for Face Swap Detection

Deepfake videos become now one of the top research topics because of their high spreading rate on social media. Faceswap, a particular type of Deepfake, consists in swapping faces of two persons in a video. Hence, face swapping can have malicious uses, such as falsifying privacy, interfering with political campaigns, terrorism, and threatening the social stability of the countries. Thus, early detection of this fake content is a primary task to limit their spread. Multiple approaches for DeepFake detection exist in the literature. The most recent and best ones are Identity-Aware and Mesoscopic features-based approaches. However, each of these approaches presents particular limitations. Therefore, in this paper, we propose to take the best out of these two recent approaches and to optimize the performance and robustness of Deepfake content detection. In particular, we propose an Ensemble Learning model based on combining the best two methods from the two aforementioned most recent approaches of detection. Our experiments show that our proposed ensemble model improved the performance and robustness of Deepfake detection to reach an accuracy of 95%.