Asymmetric Invertible Threat: Learning Reversible Privacy Defense for Face Recognition
Face Recognition systems are widely deployed in real-world applications, but they also raise privacy concerns due to unauthorized collection and misuse of facial data. Existing adversarial privacy protection methods rely on input-space perturbations to obfuscate identity information, yet their protection can degrade when adversaries learn restoration or purification mappings that partially invert the transformation. We study this setting as an asymmetric adversarial attack, in which reverse manipulation becomes feasible because existing defense paradigms do not control reversibility. To address this problem, we propose Asymmetric Reversible Face Protection (ARFP), a restoration-aware exte...
| Authors | Jiabei Zhang, Ziyuan Yang, Andrew Beng Jin Teoh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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