The advent of morphing attacks has posed significant security concerns for
automated Face Recognition systems, raising the pressing need for robust and
effective Morphing Attack Detection (MAD) methods able to effectively address
this issue. In this paper, we focus on Differential MAD (D-MAD), where a
trusted live capture, usually representing the criminal, is compared with the
document image to classify it as morphed or bona fide. We show these approaches
based on identity features are effective when the morphed image and the live
one are sufficiently diverse; unfortunately, the effectiveness is significantly
reduced when the same approaches are applied to look-alike subjects or in all
those cases when the similarity between the two compared images is high (e.g.
comparison between the morphed image and the accomplice). Therefore, in this
paper, we propose ACIdA, a modular D-MAD system, consisting of a module for the
attempt type classification, and two modules for the identity and artifacts
analysis on input images. Successfully addressing this task would allow
broadening the D-MAD applications including, for instance, the document
enrollment stage, which currently relies entirely on human evaluation, thus
limiting the possibility of releasing ID documents with manipulated images, as
well as the automated gates to detect both accomplices and criminals. An
extensive cross-dataset experimental evaluation conducted on the introduced
scenario shows that ACIdA achieves state-of-the-art results, outperforming
literature competitors, while maintaining good performance in traditional D-MAD
benchmarks.

该研究论文针对模拟攻击问题，提出了 ACIdA 模块化差分 MAD 系统，实现了较好的性能和成果，超过了现有文献中的竞争对手，并在传统差分 MAD 基准测试中保持良好的性能。