Defending Against Adversarial Fingerprint Attacks Based on Deep Image Prior

Abstract

Recently, deep learning-based biometric authentication systems, especially fingerprint authentication, have been used widely in real-world. However, these systems are vulnerable to adversarial attacks which prevent deep learning models from distinguishing input data properly. To solve these problems, various defense methods have been proposed, especially utilizing denoising mechanisms, but they provided limited defense performance. In this study, we proposed a new defense method against adversarial fingerprint attacks. To ensure defense performance, we have introduced Deep Image Prior mechanism which has superior performance in image reconstruction without prior training and a large amount of dataset. The proposed method aims to remove adversarial perturbations of the input fingerprint image and reconstruct it close to the original fingerprint image by adapting Deep Image Prior. Our method has achieved robust defense performance against various types of adversarial fingerprint attacks across different datasets, encompassing variations in sensors, shapes, and materials of fingerprint images. Furthermore, our method has demonstrated that it is superior to other image reconstruction methods.

Keywords

• Adversarial attack defense
• image reconstruction
• fingerprint authentication system
• deep learning
• denoising
• deep image prior