arXiv Computer Vision
Dorsal Hand Images for Immersive (XR) and Privacy-preserving Age Assurance and Child Safety
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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
Jul 10
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
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 Computer Vision
Sep 2
MultiGait: A Multi-Sensor Multi-Perspective Multi-Session Biometric Inference Benchmark and its Dataset
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...
arXiv Machine Learning
Sep 7
Hidden In Plain Gaze: Gaze Representations as Privacy Controls for Utility and Re-identification Risk in XR
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 Computer Vision
Sep 11
Leveraging Avatar Fingerprinting: A Multi-Generator Photorealistic Talking-Head Public Database and Benchmark
The paper introduces AVAPrintDB, a new public multi‑generator talking‑head avatar database designed for avatar fingerprinting, comprising data from two audiovisual corpora and three state‑of‑the‑art generators (GAGAvatar, LivePortrait, HunyuanPortrait). It also defines a standardized benchmark that evaluates existing avatar fingerprinting systems and explores new methods based on Foundation Models such as DINOv2 and CLIP, while analyzing performance under generator and dataset shift. The authors find that identity‑related motion cues persist across synthetic avatars, yet current fingerprinting systems are highly sensitive to changes in synthesis pipelines and source domains.
arXiv Machine Learning
Sep 4
Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification
The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.