arXiv Computer Vision
Dorsal Hand Images for Immersive (XR) and Privacy-preserving Age Assurance and Child Safety
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 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 AI
6d ago
AEGIS: Preserving Privacy of 3D Facial Avatars with Adversarial Perturbations
AEGIS is a privacy‑preserving framework that applies adversarial perturbations to the color coefficients of 3D Gaussian avatar primitives, protecting identity across all viewpoints and animations without altering geometry. It achieves near‑zero face retrieval and verification accuracy against black‑box recognizers while maintaining high perceptual realism and preserving attributes like age, gender, and emotion. The authors also introduce a viewpoint‑robust leakage metric to quantify residual identity leakage across rendered views.
Hugging Face Trending Papers
Sep 21
MIRAGE: Full-Body Bystander Privacy for Smart Glasses with Consent-Based Restoration
Video recording on smart glasses exposes more than faces. Continuous capture reveals full-body biometric signatures, including gait, posture, and silhouette, that enable person re-identification (ReID...
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.
arXiv Computer Vision
Oct 27
Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems
arXiv:2602.19539v2 Announce Type: replace Abstract: Age estimation systems are increasingly deployed as gatekeepers for age-restricted online content, yet their robustness to cosmetic modifications h...
arXiv Computer Vision
Sep 22
MIRAGE: Full-Body Bystander Privacy for Smart Glasses with Consent-Based Restoration
arXiv:2609.24537v1 Announce Type: new Abstract: Video recording on smart glasses exposes more than faces. Continuous capture reveals full-body biometric signatures, including gait, posture, and silho...