arXiv Computer Vision By Feng Xu, Xun Li, Lars Petersson, Yulei Sui, David Ahmedt-Aristizabal, Dadong Wang

TADP: Dual-Branch Temporally Adaptive Differential Privacy for Expression-Aware Video Anonymization

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TADP is a temporally adaptive differential privacy framework designed for privacy-preserving video generation with fixed-length clip-replacement adjacency. It employs a dual-branch architecture to separately extract identity and expression representations, applying temporally adaptive Gaussian mechanisms to each branch and using adaptive Rényi Differential Privacy composition to ensure joint release privacy. The framework converts clip-level RDP guarantees to (ε,δ)-differential privacy, applies DP post-processing, and demonstrates reduced identity leakage, good perceptual quality, temporal stability, and high expression recognition utility on facial expression datasets.

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arXiv Machine Learning
Aug 19

Picture the Epsilon: Pursuing Identity-Level Privacy Guarantees for Images

The paper compares four audit methods for assessing identity‑level differential privacy in pre‑trained, black‑box face generators. Each method—GaussMech, KDE‑LR, MMD‑TV, and ROC‑HT—has distinct assumptions, hyperparameters, and finite‑sample limitations, and they produce markedly different epsilon estimates when applied to FaceFusion and InstantID. The study finds that all methods reveal significant identity distinguishability, but none can be reliably ranked in this high‑distinguishability regime, suggesting that future work should evaluate them on partially private mechanisms.

By Arman Zareian Jahromi, Vishnu Bondalakunta, Mohammad Akbar Bin Shah, Naimul Haque, Shuangqing Wei, George T. Amariucai