EA-APO: A Universal Proactive Defense Against Facial Manipulation

Summary

Researchers propose EA-APO, a proactive defense that optimizes adversarial perturbations on a white-box surrogate model and applies them to source face images. The perturbations are meant to disrupt both face-swapping and face attribute editing, including against unseen black-box target models. The authors report generalization across multiple face-swapping and attribute-editing models, including commercial ones, and robustness to common post-processing and real-world social media transmission.

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