Two novel style-transfer palmprint reconstruction attacks
Two novel style-transfer palmprint reconstruction attacks
复制标题
两种新颖的风格转移掌纹重建攻击
DOI：
10.1007/s10489-022-03862-0
复制
发表时间：
2022-07
影响因子：
5.3
通讯作者：
Jun Chu
中科院分区：
文献类型：
--
作者：
Ziyuan Yang;Lu Leng;Bob Zhang;Ming Li;Jun Chu
掌纹已被广泛应用于个人身份认证，因此对识别系统的安全性评估具有重要意义。掌纹识别的在线攻击比离线攻击要困难得多，因为允许的登录和认证尝试次数较少，匹配分数不可用，训练数据较少。跨数据库攻击是另一个具有挑战性的问题，其中从模板重建的图像仍然可以有效地攻击使用其他模板的系统。为了实现在线跨库攻击并保证重建图像的高质量，提出了两种新的风格转移方法来攻击基于编码的掌纹识别系统。这两种方法都是基于卷积神经网络，但它们的优化对象不同。在第一种方法中，优化对象为输入图像，由二值模板重构出高质量的图像。在第二种方法中，使用模板数据集和只有一个风格图像来训练风格转移神经网络，以减少源域和目标域之间的风格损失。经过训练的风格转移网络每秒可以重建大约270张图像。两种方法均具有较高的攻击成功率，能够很好地满足评价系统的要求。
Palmprint has been widely used for personal authentication in many applications, such that the assessment of recognition system security is important. Online attacks of palmprint recognition are much more difficult than offline attacks due to the fewer permissible login and authentication attempts, the unusability of the matching scores, and less training data. A cross-database attack is another challenging problem, where the images reconstructed from a template can still be effective in attacking the systems with other templates. To achieve online cross-database attacks and ensure that the reconstructed images are high-quality, two novel style-transfer methods are proposed to attack coding-based palmprint recognition systems. The two methods are both based on a convolutional neural network, but their optimization objects are different. In the first method, the optimization object is the input image, where a high-quality image can be reconstructed from the binary template. In the second method, the style-transfer neural network is trained with a template dataset and only one style image to reduce the style loss between the source and target domains. The trained style-transfer network can reconstruct approximately 270 images per second. The two methods have highly impressive attack success rates and satisfactorily meet the requirements of the evaluation system.
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影响因子：
--
作者：
Fei Wang;L. Leng;A. Teoh;Jun Chu
通讯作者：
Fei Wang;L. Leng;A. Teoh;Jun Chu
DOI：
10.1109/tsmc.2016.2597291
发表时间：
2018-02
期刊：
IEEE Transactions on Systems, Man, and Cybernetics: Systems
影响因子：
--
作者：
Yong Xu;Lunke Fei;Jie Wen;D. Zhang
通讯作者：
Yong Xu;Lunke Fei;Jie Wen;D. Zhang
DOI：
10.1016/j.patcog.2017.04.016
发表时间：
2017-09
期刊：
Pattern Recognit.
影响因子：
--
作者：
Lin Zhang;Lida Li;A. Yang;Ying Shen;Meng Yang
通讯作者：
Lin Zhang;Lida Li;A. Yang;Ying Shen;Meng Yang
影响因子：
5.1
作者：
Guo, Zhenhua;Zhang, David;Zuo, Wangmeng
通讯作者：
Zuo, Wangmeng
影响因子：
5.1
作者：
Fei, Lunke;Xu, Yong;Zhang, David
通讯作者：
Zhang, David