Shaking the Fake: Detecting Deepfake Videos in Real Time via Active Probes

TL;DR

SFake is a real-time deepfake detection method that uses active physical probes to identify inconsistencies caused by deepfake manipulation, outperforming passive detection techniques in accuracy and efficiency.

Contribution

This paper introduces SFake, a novel active probing approach for deepfake detection that leverages physical interference to improve robustness and real-time performance.

Findings

SFake achieves higher detection accuracy than existing methods.

SFake operates faster with lower memory usage.

SFake effectively detects deepfakes in real-time scenarios.

Abstract

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.