Adversarial2Adversarial: Defending against Adversarial Fingerprint Attacks without Clean Images

A number of denoising-based methods have been proposed to defend against adversarial fingerprint attacks. However, these methods inherently rely on having a clean image that corresponds to each adversarial fingerprint image. In this paper, we propose a novel denoising-based defense method without the need for clean fingerprint images. Our approach leverages a Noise2Noise mechanism, which performs denoising based on the noisy dataset. This enables us to effectively eliminate any adversarial noise that may be embedded in fingerprint images without training on clean fingerprint images. The experimental results on real-world datasets confirm that our method is robust against untrained adversarial fingerprint attacks while outperforming existing methods.