Attention-enhanced wavelet high-frequency residuals for efficient privacy-preserving face imaging and recognition

Abstract

Face imaging and recognition are ubiquitous in daily applications, yet transmitting biometric face data to untrusted servers introduces critical privacy risks. Although various privacy-preserving face recognition (PPFR) methods have been proposed, they often suffer from significant degradation in both privacy performance and recognition accuracy under resource constraints. To address these challenges, we propose WHFR, an end-to-end PPFR framework integrating the discrete wavelet transform (DWT). By transforming the original image into the frequency domain via DWT, we first discard the low-frequency sub-band to obfuscate visual information. To thwart reconstruction attacks, we randomly construct high-frequency residuals, which naturally form an underdetermined system, and further combine them with stochastic sign flipping, together yielding a dual-randomization defense. To focus the downstream face recognition model on discriminative features within the perturbed residuals, we introduce a high-frequency enhancement module that employs a task-customized convolutional cosine-similarity attention mechanism, thereby preserving recognition accuracy. Experiments conducted on several benchmark datasets demonstrate that WHFR effectively safeguards visual privacy and defends against adversarial reconstruction. The accuracy dropped by only 4.84% compared with the unprotected baseline—significantly lower than the 9.34% to 12.65% accuracy loss observed in state-of-the-art methods. Moreover, WHFR substantially reduces computational overhead, enabling efficient PPFR.