Active Fake: DeepFake Camouflage

TL;DR

This paper introduces a novel framework for creating DeepFake camouflage that generates imperceptible blending inconsistencies to evade forensic detection, highlighting new security challenges in DeepFake technology.

Contribution

We propose an adversarial learning-based framework to produce effective, imperceptible DeepFake camouflage that can transfer across detectors, addressing a new security threat called Active Fake.

Findings

The method effectively creates blending inconsistencies that deceive forensic detectors.

The framework demonstrates robustness and transferability across different detection models.

Extensive experiments validate the effectiveness of the proposed DeepFake camouflage technique.

Abstract

DeepFake technology has gained significant attention due to its ability to manipulate facial attributes with high realism, raising serious societal concerns. Face-Swap DeepFake is the most harmful among these techniques, which fabricates behaviors by swapping original faces with synthesized ones. Existing forensic methods, primarily based on Deep Neural Networks (DNNs), effectively expose these manipulations and have become important authenticity indicators. However, these methods mainly concentrate on capturing the blending inconsistency in DeepFake faces, raising a new security issue, termed Active Fake, emerges when individuals intentionally create blending inconsistency in their authentic videos to evade responsibility. This tactic is called DeepFake Camouflage. To achieve this, we introduce a new framework for creating DeepFake camouflage that generates blending inconsistencies while ensuring imperceptibility, effectiveness, and transferability. This framework, optimized via an adversarial learning strategy, crafts imperceptible yet effective inconsistencies to mislead forensic detectors. Extensive experiments demonstrate the effectiveness and robustness of our method, highlighting the need for further research in active fake detection.