A Case Study on how Beautification Filters Can Fool Deepfake Detectors

The exponential growth of shared multimedia con-tent made necessary algorithms for deepfake detection. At the same time, beautification filters have become a popular tool and new filters are released every day. Therefore, is it possible to fool state-of-the-art (SotA) detectors by simply applying a beautification filter to the manipulated video? In this paper, we study the impact of beautification filters on Celeb-DF-B, a novel database created by applying popular social media beautification filters to a subset of real and fake videos from the Celeb-DF dataset. We evaluated the effect of beautification on three SotA passive deepfake detectors and on human evaluators. The results indicate that filters significantly alter the behavior of the three detectors studied, resulting in a notable decrease in the video-level AUC on the beautified subset of Celeb-DF-B. In the context of human-level performance, the use of filters similarly influences human decision-making, affecting the accurate categorization of videos as either real or fake.