论文标题
面部变形攻击和面部图像质量：形变的效果和无监督的攻击检测质量
Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality
论文作者
论文摘要
变形攻击是一种表现攻击的一种形式，近年来引起了人们越来越多的关注。可以成功验证变形的图像到多个身份。因此，此操作提出了与旅行或身份文件的能力有关的严重安全问题。以前的作品涉及了变形攻击图像质量的问题，但是，主要目的是定量证明产生的变形攻击的现实外观。我们认为，与真正的样品相比，变形过程可能会影响面部识别（FR）中的感知图像质量和图像实用程序。为了研究这一理论，这项工作对变形对面部图像质量的影响进行了广泛的分析，包括一般图像质量度量和面部图像实用程序测量。该分析不仅限于单个变形技术，而是使用十种不同的质量测量方法来研究六种不同的变形技术和五种不同的数据源。该分析揭示了变形攻击的质量得分与通过某些质量度量测量的真正样本的质量得分之间的一致性。我们的研究进一步建立在这种效果的基础上，并研究基于质量得分进行无监督的变形攻击检测（MAD）的可能性。我们的研究探讨了intra和数据库间的可检测性，以评估这种检测概念在不同的变形技术和善意来源上的普遍性。我们的最终结果指出，一组质量测量（例如岩体和CNNIQA）可用于执行无监督和普遍的MAD，正确分类精度超过70％。
Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility measures. This analysis is not limited to a single morphing technique, but rather looks at six different morphing techniques and five different data sources using ten different quality measures. This analysis reveals consistent separability between the quality scores of morphing attack and bona fide samples measured by certain quality measures. Our study goes further to build on this effect and investigate the possibility of performing unsupervised morphing attack detection (MAD) based on quality scores. Our study looks intointra and inter-dataset detectability to evaluate the generalizability of such a detection concept on different morphing techniques and bona fide sources. Our final results point out that a set of quality measures, such as MagFace and CNNNIQA, can be used to perform unsupervised and generalized MAD with a correct classification accuracy of over 70%.
