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.
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.
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...
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: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: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...
arXiv:2607. 08402v1 Announce Type: cross Abstract: Large-scale and diverse datasets are needed to train AI models to take real-time decisions for autonomous vehicles (AVs), an intelligent transportation system (ITS) application.
arXiv:2610.09288v1 Announce Type: cross Abstract: Many rare genetic diseases are associated with recognizable craniofacial features. However, traditional approaches for describing facial morphology r...
The paper introduces XAVAD, a unified approach for explainable anomaly detection in anonymized crowd video monitoring. It combines zero‑shot anonymization, anomaly type categorization, spatial localization, and scene context description using multimodal CLIP embeddings, LLM‑assisted prompting, and a prompt‑guided patching autofocus mechanism. Experiments on public CCTV datasets show XAVAD outperforms state‑of‑the‑art methods and offers insights into privacy‑explainability trade‑offs.
arXiv:2609.01036v1 Announce Type: cross Abstract: A lack of suitable datasets has limited the research into the privacy risks of novel smart city sensors, such as thermal cameras, depth cameras, and...
The paper investigates how different gaze data representations affect privacy and utility in extended reality (XR) systems. Three representations—raw gaze, spatial attention heatmaps, and engineered eye‑movement features—are compared using the HoloAssist dataset, evaluating action recognition accuracy and closed‑set user re‑identification. Engineered features preserve about 85% of action‑recognition performance while reducing re‑identification risk by roughly an order of magnitude, yet still leave some identity leakage, indicating that abstraction alone does not guarantee privacy.
arXiv:2610.19717v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-fidelity, identity-preserving image generation for personalized applications such as digital avat...