Dealing with Subject Similarity in Differential Morphing Attack Detection

TL;DR

This paper introduces ACIdA, a modular system for differential morphing attack detection that improves accuracy especially in high-similarity cases, enhancing security in face recognition systems.

Contribution

Proposes ACIdA, a novel modular D-MAD system that effectively handles subject similarity issues, expanding application scenarios beyond traditional benchmarks.

Findings

Achieves state-of-the-art results on cross-dataset evaluations.

Outperforms existing methods in high-similarity scenarios.

Maintains strong performance in traditional benchmarks.

Abstract

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.