Vulnerability of Face Morphing Attacks: A Case Study on Lookalike and Identical Twins

24 Mar 2023  ·  Raghavendra Ramachandra, Sushma Venkatesh, Gaurav Jaswal, Guoqiang Li ·

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.

PDF Abstract


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here