Morphing Attack Detection-Database, Evaluation Platform, and Benchmarking
Morphing Attack Detection-Database, Evaluation Platform, and Benchmarking
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DOI：
10.1109/tifs.2020.3035252
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发表时间：
2020-06
影响因子：
6.8
通讯作者：
K. Raja;Matteo Ferrara;Annalisa Franco;L. Spreeuwers;Ilias Batskos;Florens de Wit;M. Gomez-Barrero;U. Scherhag;Daniel Fischer;S. Venkatesh;Jag Mohan Singh;Guoqiang Li;Loïc Bergeron;Sergey Isadskiy;Raghavendra Ramachandra;C. Rathgeb;Dinusha Frings;U. Seidel;F. Knopjes;R. Veldhuis;D. Maltoni;C. Busch
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文献类型：
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作者：
K. Raja;Matteo Ferrara;Annalisa Franco;L. Spreeuwers;Ilias Batskos;Florens de Wit;M. Gomez-Barrero;U. Scherhag;Daniel Fischer;S. Venkatesh;Jag Mohan Singh;Guoqiang Li;Loïc Bergeron;Sergey Isadskiy;Raghavendra Ramachandra;C. Rathgeb;Dinusha Frings;U. Seidel;F. Knopjes;R. Veldhuis;D. Maltoni;C. Busch
变形攻击对人脸识别系统构成了严重的威胁。尽管在最近的工作中报告了一些进展，但我们注意到严重的开放性问题，如独立基准，普遍性挑战和对年龄，性别，种族的考虑没有得到充分解决。变形攻击检测（MAD）算法往往是易于推广的挑战，因为他们是数据库依赖。现有的数据库大多是半公开性质的，在种族、各种变形过程和后处理管道方面缺乏多样性。此外，它们没有反映自动边境控制（ABC）的现实操作场景，也没有提供在看不见的数据上测试MAD的基础，以衡量算法的鲁棒性。在这项工作中，我们提出了一个新的隔离数据集，以促进MAD的进步，其中算法可以在看不见的数据上进行测试，以更好地推广。新构建的数据集由来自不同种族，年龄组和性别的150名受试者的面部图像组成。为了挑战现有的MAD算法，变形的图像具有从贡献图像创建的仔细的主题预先选择，并且进一步后处理以去除变形伪影。这些图像也被打印和扫描，以消除所有数字线索，并模拟MAD算法的现实挑战。此外，我们提出了一个新的在线评估平台，以测试算法的隔离数据。利用该平台可以对形态检测算法进行性能测试和泛化能力研究。这项工作还提出了一个详细的分析各种子集的隔离数据，并概述了开放的挑战，在MAD研究的未来方向。
Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.