Li Hailin, Ramachandra Raghavendra
Norwegian University of Science and Technology (NTNU), 2815, Gjovik, Norway.
Sci Rep. 2024 Jul 13;14(1):16182. doi: 10.1038/s41598-024-66790-8.
Smartphone-based biometric authentication has been widely used in various applications. Among several biometric characteristics, fingerphoto biometrics captured from smartphones are gaining popularity owing to their usability, scalability across different smartphones, and reliable verification. However, fingerphoto verification systems are vulnerable to both direct and indirect attacks. In this work, we propose a novel method to generate morphing attacks on fingerphoto biometrics captured using smartphones. We introduce three different image-level fingerphoto morphing attack generation algorithms that can generate high-quality fingerphoto morphing images with minimum distortions. Extensive experiments were conducted on two datasets captured using different smartphones under various environmental conditions. The results demonstrate that the proposed morphing algorithms are highly vulnerable to commercial off-the-shelf and block-directional fingerprint verification systems. To effectively detect morphing attacks on fingerphoto biometrics, we propose the use of fingerphoto morphing attack detection algorithms that utilize both handcrafted and deep features. However, our detection results showed a high error rate in accurately detecting these types of attacks.
基于智能手机的生物特征认证已在各种应用中广泛使用。在多种生物特征中，从智能手机捕获的手指照片生物特征因其可用性、在不同智能手机上的可扩展性以及可靠的验证而越来越受欢迎。然而，手指照片验证系统容易受到直接和间接攻击。在这项工作中，我们提出了一种新颖的方法来对使用智能手机捕获的手指照片生物特征进行变形攻击。我们介绍了三种不同的图像级手指照片变形攻击生成算法，这些算法可以生成具有最小失真的高质量手指照片变形图像。在不同环境条件下使用不同智能手机捕获的两个数据集上进行了广泛的实验。结果表明，所提出的变形算法对商用现成的和块方向指纹验证系统高度脆弱。为了有效检测对手指照片生物特征的变形攻击，我们建议使用同时利用手工特征和深度特征的手指照片变形攻击检测算法。然而，我们的检测结果显示在准确检测这些类型的攻击方面错误率很高。
