A feature-space method that erases usable identity information from face images via learnable perturbations and a Face Revive Generator, rendering them ineffective for deepfake swapping while preserving visual quality.
Anti- forgery: Towards a stealthy and robust deepfake disruption attack via adversarial perceptual-aware perturbations
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 3years
2026 3representative citing papers
DAWF embeds identity watermarks via a parallel multi-face architecture and uses selective loss to answer which face was forged and whose identity was used.
Pixel-level protective perturbations for portrait privacy are ineffective against common image transformations, and a low-cost purification framework can strip them out.
citing papers explorer
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ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation
A feature-space method that erases usable identity information from face images via learnable perturbations and a Face Revive Generator, rendering them ineffective for deepfake swapping while preserving visual quality.
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Which Face and Whose Identity? Solving the Dual Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
DAWF embeds identity watermarks via a parallel multi-face architecture and uses selective loss to answer which face was forged and whose identity was used.
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Do Protective Perturbations Really Protect Portrait Privacy under Real-world Image Transformations?
Pixel-level protective perturbations for portrait privacy are ineffective against common image transformations, and a low-cost purification framework can strip them out.