An Enhanced Palmprint Adversarial Attack Against Visible and Invisible Features

Adversarial attacks on palmprint recognition are crucial because these attacks can manipulate palmprint images to bypass authentication systems, posing security threats. However, many existing adversarial attacks overlook the unique features of palmprint. In this paper, we propose an enhanced palmprint adversarial attack against visible and invisible features. First, we focus on extracting palmprint main lines that are crucial for targeted adversarial attacks. Second, we introduce a channel attention mechanism that can effectively emphasize the invisible features in the palmprint image. We fuse these two features to achieve a more effective attack, ensuring that both visible and invisible details contribute to the enhancement of the adversarial attack. Finally, adversarial examples generated by our method are incorporated into the training process. The experimental results demonstrate the effectiveness of our enhanced attack.