Hugging Face Trending Papers July 20, 2026

DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition

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Split face recognition reduces client-side computation but exposes intermediate features to feature inversion attacks and unauthorized analysis by honest-but-curious (HBC) servers. Existing privacy-preserving face recognition methods mainly aim to resist unauthorized reconstruction, typically producing features whose inversion yields visibly degraded results, which may reveal the existence of protection and motivate adaptive attacks.

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safety

arXiv AI

Jul 21

DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition

arXiv:2607. 17504v1 Announce Type: cross Abstract: Split face recognition reduces client-side computation but exposes intermediate features to feature inversion attacks and unauthorized analysis by honest-but-curious (HBC) servers.

By Zhihan Ren, Lijun He, Xinyao Wang, Xinzhu Fu, Fan Li

safety

arXiv Computer Vision

3d ago

FaceLinkGen: A Re-evaluation of Identity Leakage in Privacy-Preserving Face Recognition and Face Anonymization Systems Using Simple Distillation

arXiv:2602.02914v4 Announce Type: replace Abstract: Privacy-preserving face recognition (PPFR) and face anonymization have different goals, but both must retain some identity-related information for...

By Wenqi Guo, Qingyun Qian, Mohamed Shehata, Shan Du

rag efficiency safety

arXiv Machine Learning

Aug 6

Model Inversion meets Cryptographic Fuzzy Extractors

arXiv:2510. 25687v4 Announce Type: replace-cross Abstract: Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning (ML) models.

By Mallika Prabhakar, Louise Xu, Prateek Saxena

rag safety

arXiv AI

Aug 3

Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership

arXiv:2607. 29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition.

By Pawe{\l} Borsukiewicz, Daniele Lunghi, Wendk\^uuni C. Ou\'edraogo, Jacques Klein, Tegawend\'e F. Bissyand\'e

rag safety

arXiv Machine Learning

Jun 2

Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?

arXiv:2409. 01062v4 Announce Type: replace Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models.

By Viet-Hung Tran, Ngoc-Bao Nguyen, Son T. Mai, Hans Vandierendonck, Ira Assent, Alex Kot, Ngai-Man Cheung

benchmarks safety

arXiv Computer Vision

Aug 25

Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection

arXiv:2608.21455v1 Announce Type: new Abstract: Face presentation attack detection (PAD) is traditionally formulated as a face-specific problem, although many of the visual artifacts introduced by pr...

By Guray Ozgur, Fadi Boutros, Naser Damer

benchmarks safety

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Related stories

DecoyFace: Beyond Obfuscation via Controllable and Imperceptible Identity Misdirection for Privacy-Preserving Face Recognition

FaceLinkGen: A Re-evaluation of Identity Leakage in Privacy-Preserving Face Recognition and Face Anonymization Systems Using Simple Distillation

Model Inversion meets Cryptographic Fuzzy Extractors

Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership

Random Erasing vs. Model Inversion: A Promising Defense or a False Hope?

Tomatoes, Potatoes, and Onions: Questioning the Need for Faces in Face Presentation Attack Detection