Local Features Meet Stochastic Anonymization: Revolutionizing Privacy-Preserving Face Recognition for Black-Box Models

TL;DR

This paper introduces a novel privacy-preserving face recognition method that enhances local features and employs stochastic anonymization, achieving high accuracy on black-box models while resisting adversarial reconstruction.

Contribution

It proposes a new approach that focuses on local features and uses irreversible stochastic injection to improve privacy and generalization in black-box face recognition models.

Findings

Achieves 94.21% accuracy on black-box models.

Outperforms existing methods in privacy protection.

Resists adversarial reconstruction attacks.

Abstract

The task of privacy-preserving face recognition (PPFR) currently faces two major unsolved challenges: (1) existing methods are typically effective only on specific face recognition models and struggle to generalize to black-box face recognition models; (2) current methods employ data-driven reversible representation encoding for privacy protection, making them susceptible to adversarial learning and reconstruction of the original image. We observe that face recognition models primarily rely on local features ({e.g., face contour, skin texture, and so on) for identification. Thus, by disrupting global features while enhancing local features, we achieve effective recognition even in black-box environments. Additionally, to prevent adversarial models from learning and reversing the anonymization process, we adopt an adversarial learning-based approach with irreversible stochastic injection to ensure the stochastic nature of the anonymization. Experimental results demonstrate that our method achieves an average recognition accuracy of 94.21\% on black-box models, outperforming existing methods in both privacy protection and anti-reconstruction capabilities.