Synthesizing Black-box Anti-forensics DeepFakes with High Visual Quality
Bing Fan Shu Hu Feng Ding
DeepFake, an AI technology for creating facial forgeries, has garnered global
attention. Amid such circumstances, forensics researchers focus on developing
defensive algorithms to counter these threats. In contrast, there are
techniques developed for enhancing the aggressiveness of DeepFake, e.g.,
through anti-forensics attacks, to disrupt forensic detectors. However, such
attacks often sacrifice image visual quality for improved undetectability. To
address this issue, we propose a method to generate novel adversarial
sharpening masks for launching black-box anti-forensics attacks. Unlike many
existing arts, with such perturbations injected, DeepFakes could achieve high
anti-forensics performance while exhibiting pleasant sharpening visual effects.
After experimental evaluations, we prove that the proposed method could
successfully disrupt the state-of-the-art DeepFake detectors. Besides, compared
with the images processed by existing DeepFake anti-forensics methods, the
visual qualities of anti-forensics DeepFakes rendered by the proposed method
are significantly refined.