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.

By Hui Wei, Hao Yu, Hui Kuurila-Zhang, Guoying Zhao
arXiv AI
Jul 8

TILDE: TILt-based Distributional Erasure for Concept Unlearning

arXiv:2607. 06432v1 Announce Type: cross Abstract: Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and safety regulations, deployed systems must be able to suppress unwanted concepts after training.

By Naveen George, Naoki Murata, Yuhta Takida, Konda Reddy Mopuri, Yuki Mitsufuji