Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection

TL;DR

This paper introduces FingerSafe, a hierarchical perceptual noise injection method that effectively protects fingerprint privacy in social media images by perturbing high-level semantics while maintaining visual naturalness.

Contribution

FingerSafe is the first framework to use hierarchical perceptual noise injection for fingerprint privacy protection, improving black-box transferability and visual naturalness.

Findings

Achieves up to 94.12% fingerprint protection in digital scenarios.

Provides up to 68.75% protection in social media scenarios.

Balances privacy protection with image quality preservation.

Abstract

Billions of people are sharing their daily life images on social media every day. However, their biometric information (e.g., fingerprint) could be easily stolen from these images. The threat of fingerprint leakage from social media raises a strong desire for anonymizing shared images while maintaining image qualities, since fingerprints act as a lifelong individual biometric password. To guard the fingerprint leakage, adversarial attack emerges as a solution by adding imperceptible perturbations on images. However, existing works are either weak in black-box transferability or appear unnatural. Motivated by visual perception hierarchy (i.e., high-level perception exploits model-shared semantics that transfer well across models while low-level perception extracts primitive stimulus and will cause high visual sensitivities given suspicious stimulus), we propose FingerSafe, a hierarchical perceptual protective noise injection framework to address the mentioned problems. For black-box transferability, we inject protective noises on fingerprint orientation field to perturb the model-shared high-level semantics (i.e., fingerprint ridges). Considering visual naturalness, we suppress the low-level local contrast stimulus by regularizing the response of Lateral Geniculate Nucleus. Our FingerSafe is the first to provide feasible fingerprint protection in both digital (up to 94.12%) and realistic scenarios (Twitter and Facebook, up to 68.75%). Our code can be found at https://github.com/nlsde-safety-team/FingerSafe.