2024
DOI: 10.1609/aaai.v38i1.27762
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Adversarial Robust Safeguard for Evading Deep Facial Manipulation

Jiazhi Guan,
Yi Zhao,
Zhuoer Xu
et al.

Abstract: The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur.… Show more

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