Author

G. Amariucai

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Preprint Aug 2026

Toward Interpretable Privacy Guarantees in Face-Swapping Anonymization

A linear stochastic model is proposed that treats face-swappers as transformations on the space of identity embeddings, providing an interpretable account of the leakage mechanism, thus making formal privacy guarantees explainable -- and perfectible -- rather than purely observational.

Vishnu Bondalakunta, Arman Zareian Jahromi, Shuangqing Wei et al. · 0 citations

#machine learning Preprint Aug 2026

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

A comparative study of four audits applicable to pre-trained, black-box face generators, which consistently reveal substantial identity distinguishability while reporting markedly different epsilon estimates that reflect each method's distinct assumptions and finite-sample treatment.

Arman Zareian Jahromi, Vishnu Bondalakunta, M. Shah et al. · 0 citations