Palm-vein images reconstruction against adversarial attacks

Palm-vein has received widespread attention for reliable biometric recognition due to its robust resistance to replicate and forge. However, the rise of adversarial attacks poses a high risk of vulnerability for palm-vein recognition, leaving most existing methods vulnerable to small and human-imperceptible adversarial perturbations. In this paper, we propose a palm-vein image reconstruction network for palm-vein image protection, which mainly consists of palm-vein-specific exploration, feature refinement, and image reconstruction sub-networks. Specifically, we first specially learn the noise-insensitive palm-vein-specific feature by decoupling non-vein noise information via cascaded noise-injected and Canny-based convolution layers, and then refine palm-vein-specific features via multiple stacked basic convolution and transposed convolution pairs. Lastly, we convert the fine-grained palm-vein features into the latent sharp palm-vein images via two transposed convolution layers. Moreover, we develop both identity-aware and visual-aware loss functions to ensure the high-quality of the reconstructed palm-vein images. Experimental results on the widely used PolyU palm-vein dataset demonstrate the promising effectiveness of the proposed palm-vein image reconstruction network.