FaceObfuscator: Defending Deep Learning-based Privacy Attacks with Gradient Descent-resistant Features in Face Recognition

As face recognition is widely used in various security-sensitive scenarios, face privacy issues are receiving increasing attention. Recently, many face recognition works have focused on privacy preservation and converted the original images into protected facial features. However, our study reveals that emerging Deep Learning-based (DL-based) reconstruction attacks exhibit notable ability in learning and removing the protection patterns introduced by existing schemes and recovering the original facial images, thus posing a significant threat to face privacy. To address this threat, we introduce FaceObfuscator, a lightweight privacy-preserving face recognition system that first removes visual information that is non-crucial for face recognition from facial images via frequency domain and then generates obfuscated features interleaved in the feature space to resist gradient descent in DL-based reconstruction attacks. To minimize the loss in face recognition accuracy, obfuscated features with different identities are well-designed to be interleaved but non-duplicated in the feature space. This non-duplication ensures that FaceObfuscator can extract identity information from the obfuscated features for accurate face recognition. Extensive experimental results demonstrate that FaceObfuscator's privacy protection capability improves around 90% compared to existing privacy-preserving methods in two major leakage scenarios including channel leakage and database leakage, with a negligible 0.3% loss in face recognition accuracy. Our approach has also been evaluated in a real-world environment and protected more than 100K people's face data of a major university.