The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios

Face morphing attacks pose an increasing threat to face recognition (FR) systems. A morphed photo contains biometric information from two different subjects to take advantage of vulnerabilities in FRs. These systems are particularly susceptible to attacks when the morphs are subjected to print-scanning to mask the artifacts generated during the morphing process. We investigate the impact of print-scanning on morphing attack detection through a series of evaluations on heterogeneous morphing attack scenarios. Our experiments show that we can increase the Mated Morph Presentation Match Rate (MMPMR) by up to 8.48%. Furthermore, when a Single-image Morphing Attack Detection (S-MAD) algorithm is not trained to detect print-scanned morphs the Morphing Attack Classification Error Rate (MACER) can increase by up to 96.12%, indicating significant vulnerability.

1. Introduction

Face recognition (FR) systems have become one of the most widely used biometric modalities, ranging from security to identification applications in government offices, law enforcement, and visa management. These systems are highly effective in preventing unauthorized access while maintaining low false rejection and acceptance rates, placing them among the best methods to reduce security vulnerabilities [1] – [3]. However, these systems still have susceptibilities, notably in the presence of face-morphing attacks. Face morphing attacks aim to exploit the intrinsic nature of FR classifiers that map biometric templates to a singular identity in a one-to-one map. To achieve this, an attacker creates a single morphed face image incorporating the biometric traits and facial landmarks of two different identities. The morphed image can cause an FR system to incorrectly register a false accept with both identities [3] – [7].