Improving Accomplice Detection in the Morphing Attack

This paper addresses a critical security challenge in the field of automated face recognition, i.e., morphing attack. The paper introduces a novel differential morphing attack detection (D-MAD) system called ACIdA, which is specifically designed to overcome the limitations of existing D-MAD approaches. Traditional methods are effective when the morphed image and live capture are distinct, but they falter when the morphed image closely resembles the accomplice. This is a critical gap because detecting accomplice involvement in addition to the criminal one is essential for robust security. ACIdA′s impact is underscored by its innovative approach, which consists of three modules: One for classifying the type of attempt (bona fide, criminal, or accomplice verification attempt), and two others dedicated to analyzing identity and artifacts. This multi-faceted approach enables ACIdA to excel in scenarios where the morphed image does not equally represent both contributing subjects — a common and challenging situation in real-world applications. The paper′s extensive cross-dataset experimental evaluation demonstrates that ACIdA achieves state-of-the-art results in detecting accomplices, a crucial advancement for enhancing the security of face recognition systems. Furthermore, it maintains strong performance in identifying criminals, thereby addressing a significant vulnerability in current D-MAD methods and marking a substantial contribution to the field of facial recognition security.