A face morphing attack image can be verified to multiple identities, making this attack a major vulnerability to processes based on identity verification, such as border checks. Different methods have been proposed to detect face morphing attacks, however, with low generalizability to unexpected post-morphing processes. A major post-morphing process is the print and scan operation performed in many countries when issuing a passport or identity document. In this work, we address this generalization problem by adapting a pixel-wise supervision approach where we train a network to classify each pixel of the image into an attack or not during the training process, rather than only having one label for the whole image. Our pixel-wise morphing attack detection (PW-MAD) solution performs more accurately than a set of established baselines. More importantly, our approach shows high generalizability in comparison to related works, when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD approach, we create a new face morphing attack dataset with digital and re-digitized attacks and bona fide samples, namely the LMA-DRD dataset that will be made publicly available for research purposes.
翻译:脸部变形图像可以被验证为多重身份, 使这次攻击成为主要易受身份验证程序( 如边境检查) 的伤害。 但是, 提出了不同的方法来检测脸部变形袭击, 且对意外变形后进程一般化程度较低。 一个主要的变形后进程是许多国家在签发护照或身份证件时进行的打印和扫描操作。 在这项工作中, 我们通过调整像素学监督方法来解决这个一般化问题, 即我们训练一个网络, 将图像的每个像素分类为攻击或不在训练过程中进行攻击, 而不是只给整个图像贴上一个标签。 我们的像素型攻击探测( PW-MAD) 解决方案比一套既定基线要精确得多。 更重要的是, 我们的方法显示, 在对未知的重新数字化袭击或身份证件进行评估时, 与相关工程相比, 高度的通用性。 除了我们的PW- MAD 方法外, 我们创造了一个新的面形变形攻击数据集, 包括数字和重新数字化攻击以及真实的样本, 即LMA- DD数据集, 将公开用于研究目的 。