Privacy-Preserving Multi-Soft Biometrics through Generative Adversarial Networks with Chaotic Encryption

The rapid advances in deep learning have enabled the extraction of soft-biometric attributes from faces, raising privacy concerns regarding images collected for face recognition. To address this issue, we propose a privacy-preserving scheme for multiple soft-biometric attributes using Generative Adversarial Networks (GANs), aiming to effectively protect individual privacy while maintaining face recognition system performance. Our scheme incorporates two main modules: 1) The attribute encryption system, which identifies attributes to be hidden through chaotic cryptography and supports the recovery of original attributes. 2) Encoder features are adaptively transformed using Selective Transmission Units (STUs) within a generator featuring an encoder-decoder structure to generate perturbed images. The generated perturbed images are visually similar to the original images but differ in soft-biometric attributes. Experimental results show that our method maintains high face recognition accuracy while preserving privacy, and the quality of the generated images surpasses existing techniques.