Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes
This addresses the issue of malicious deepfake misuse in scenarios like web conferences and identity authentication, offering a novel detection approach, though it is incremental as it builds on existing deepfake detection efforts.
The paper tackles the problem of detecting deepfake videos in real-time by proposing SFake, a method that uses active probes to trigger mechanical vibrations on smartphones, achieving higher detection accuracy, faster processing speed, and lower memory consumption compared to six other methods.
Real-time deepfake, a type of generative AI, is capable of "creating" non-existing contents (e.g., swapping one's face with another) in a video. It has been, very unfortunately, misused to produce deepfake videos (during web conferences, video calls, and identity authentication) for malicious purposes, including financial scams and political misinformation. Deepfake detection, as the countermeasure against deepfake, has attracted considerable attention from the academic community, yet existing works typically rely on learning passive features that may perform poorly beyond seen datasets. In this paper, we propose SFake, a new real-time deepfake detection method that innovatively exploits deepfake models' inability to adapt to physical interference. Specifically, SFake actively sends probes to trigger mechanical vibrations on the smartphone, resulting in the controllable feature on the footage. Consequently, SFake determines whether the face is swapped by deepfake based on the consistency of the facial area with the probe pattern. We implement SFake, evaluate its effectiveness on a self-built dataset, and compare it with six other detection methods. The results show that SFake outperforms other detection methods with higher detection accuracy, faster process speed, and lower memory consumption.