arXiv AI
AEGIS: Preserving Privacy of 3D Facial Avatars with Adversarial Perturbations
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arXiv Computer Vision
Oct 15
No Way To Steal My Face: Proactive Defense Against Identity-Preserving Personalized Generation
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...
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 28
PrinGS: Privacy Preservation in Gaussian Splatting
PrinGS introduces a privacy‑preserving framework for 3D Gaussian Splatting, enabling post‑training anonymization of sensitive content such as faces, license plates, documents, and computer screens. By localizing and directly anonymizing regions in the 3D representation, the method removes sensitive information from all rendered novel views while maintaining scene geometry and visual fidelity, achieving this in under one second. The approach also offers a privacy‑by‑design training strategy that prevents sensitive data from being encoded during optimization, yielding splats with low face similarity and unreadable text.
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
Aug 26
Source-Face Authenticity Detection for 3D Gaussian Heads Reconstructed from a Single Portrait: A Benchmark and Dedicated Detector
arXiv:2608.23984v1 Announce Type: new Abstract: Recent advances in single-image 3D Gaussian head reconstruction have enabled highly realistic and freely renderable digital heads from a single portrai...
arXiv Computer Vision
Sep 1
Audio-Driven Adversarial Defense for 3D Talking Face Generation with totally Visual Fidelity Preservation
arXiv:2608.30951v1 Announce Type: new Abstract: The rapid development of generative portrait models has raised growing concerns about privacy leakage and identity misuse. In particular, audio-driven...