ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation
Junyan Luo, Peipeng Yu, Jianwei Fei, Shiya Zeng, Xiaoyu Zhou, Zhihua Xia, Xiang Liu

TL;DR
ID-Eraser is a proactive face protection method that disrupts identity recognition in face swapping models by perturbing identity embeddings, producing realistic images while preventing malicious swaps.
Contribution
It introduces a feature-space defense that effectively neutralizes face swapping attacks by perturbing identity embeddings and reconstructing natural-looking protected images.
Findings
Substantially reduces identity recognition accuracy in face swapping systems.
Achieves lowest Top-1 accuracy of 0.30 and high visual quality metrics.
Demonstrates robustness across datasets, distortions, and commercial APIs.
Abstract
Deepfake technologies have rapidly advanced with modern generative AI, and face swapping in particular poses serious threats to privacy and digital security. Existing proactive defenses mostly rely on pixel-level perturbations, which are ineffective against contemporary swapping models that extract robust high-level identity embeddings. We propose ID-Eraser, a feature-space proactive defense that removes identifiable facial information to prevent malicious face swapping. By injecting learnable perturbations into identity embeddings and reconstructing natural-looking protection images through a Face Revive Generator (FRG), ID-Eraser produces visually realistic results for humans while rendering the protected identities unusable for Deepfake models. Experiments show that ID-Eraser substantially disrupts identity recognition across diverse face recognition and swapping systems under strict…
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