arXiv Computer Vision
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jul 16
PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors
arXiv:2502. 16167v2 Announce Type: replace-cross Abstract: Diffusion models (DMs) have advanced text-to-image (T2I) synthesis, yet their personalization capabilities raise serious privacy and copyright concerns.
arXiv Computer Vision
Oct 2
Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision--Language Models
The paper introduces Anti-Persona, an image-level defense that prevents unauthorized identity binding and recognition in personalized vision–language models. By aggregating visual features from multiple reference images into an identity prototype and applying subtle perturbations, the method disrupts prototype alignment in the vision‑encoder space while maintaining visual fidelity. Experiments on two personalized LVLMs show protection rates up to 95.0% and robustness across prompt variations and encoder mismatches.
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.
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.
arXiv AI
Jul 29
Face De-Identification: A Domain-Centric Survey from Capture to Processing
arXiv:2607. 25926v1 Announce Type: cross Abstract: Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition while preserving utility for downstream tasks.
arXiv Computer Vision
Oct 8
How Private is Private? A Comparative Study for Face De-Identification
The paper introduces a unified evaluation framework for face de-identification (FDeID), addressing inconsistencies in current protocols. It presents UtilFace, a demographically balanced benchmark assembled from four large-scale datasets, and HiFD, a hierarchical metric that simultaneously measures identity suppression, multi-level utility preservation, and image quality. Using this framework, the authors compare adversarial, GAN-based, and diffusion-based methods, revealing trade-offs and failure modes that were previously hidden, and they release the benchmark and toolkit for reproducible research.