arXiv Computer Vision September 24, 2026 By Felix Rosberg, Cristofer Englund, Eren Erdal Aksoy, Fernando Alonso-Fernandez

Adversarial Attacks and Identity Leakage in De-Identification Systems: An Empirical Study

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arXiv AI

Aug 3

DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models

arXiv:2607. 28936v1 Announce Type: cross Abstract: Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space.

By Omid Ahmadieh, Nima Karimian

rag diffusion benchmarks 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

Jun 11

Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks

arXiv:2606. 11615v1 Announce Type: cross Abstract: The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent.

By Omid Ahmadieh, Nima Karimian

llms rag diffusion robotics fine-tuning multimodal benchmarks safety

arXiv Computer Vision

Sep 24

HYDRO: Towards Non-Reversible Face De-Identification Using a High-Fidelity Hybrid Diffusion and Target-Oriented Approach

arXiv:2609.27011v1 Announce Type: new Abstract: Target-oriented face de-identification models aim to anonymize the identity of a target individual across different images or video frames, such that t...

By Felix Rosberg, Vitomir \v{S}truc, Cristofer Englund, Eren Erdal Aksoy, Fernando Alonso-Fernandez

diffusion benchmarks

arXiv Machine Learning

Jul 15

Reducing information dependency does not cause training data privacy. Adversarially non-robust features do

arXiv:2607. 12354v1 Announce Type: new Abstract: In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks.

By Rasmus Torp, Shailen K. Smith, Adam Breuer

benchmarks safety

Hugging Face Trending Papers

Aug 17

Training-Free Reconstruction-Based AI-Generated Image Detectors Are Inherently Vulnerable to Adversarial Examples

The impressive visual quality and ubiquity of AI-generated images call for reliable and robust detection methods. Reconstruction-based detectors have emerged as a promising direction for transparent and training-free identification of synthetic images.