Weakly-Supervised Prompt-Guided Privacy Enhancement for Soft-Biometric Attributes in Facial Images

Summary

Researchers propose IFSNet, a weakly supervised, prompt-driven framework that enhances image-level privacy in face images by obscuring soft-biometric attributes such as age, gender, and race while preserving identity utility. It needs no per-image sensitive-attribute annotations during training or inference. The authors report that across five face datasets and three identity-labeled datasets, it reduces sensitive-attribute inference while retaining verification performance, measured by equal error rate and false non-match rate, with a favorable privacy-utility trade-off.