Vulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins
This study addresses security risks in automatic border control by analyzing morphing attacks with specific data subjects, representing an incremental contribution to biometric security.
This work investigated the vulnerability of Face Recognition Systems (FRS) to face morphing attacks using lookalike and identical twins, finding that such attacks pose a significant threat, with experiments benchmarking their potential compared to normal morphing.
Face morphing attacks have emerged as a potential threat, particularly in automatic border control scenarios. Morphing attacks permit more than one individual to use travel documents that can be used to cross borders using automatic border control gates. The potential for morphing attacks depends on the selection of data subjects (accomplice and malicious actors). This work investigates lookalike and identical twins as the source of face morphing generation. We present a systematic study on benchmarking the vulnerability of Face Recognition Systems (FRS) to lookalike and identical twin morphing images. Therefore, we constructed new face morphing datasets using 16 pairs of identical twin and lookalike data subjects. Morphing images from lookalike and identical twins are generated using a landmark-based method. Extensive experiments are carried out to benchmark the attack potential of lookalike and identical twins. Furthermore, experiments are designed to provide insights into the impact of vulnerability with normal face morphing compared with lookalike and identical twin face morphing.