Morphing Attack Potential

Abstract

In security systems the risk assessment in the sense of common criteria testing is a very relevant topic; this requires quantifying the attack potential in terms of the expertise of the attacker, his knowledge about the target and access to equipment. Contrary to those attacks, the recently revealed morphing attacks against Face Recognition Systems (FRSs) can not be assessed by any of the above criteria. But not all morphing techniques pose the same risk for an operational face recognition system. This paper introduces with the Morphing Attack Potential (MAP) a consistent methodology, that can quantify the risk, which a certain morphing attack creates.

This paper is a preprint of a paper accepted by IEEE International Workshop on Biometrics and Forensics (IWBF 2022). When the final version is published, the copy of record will be available at the IEEE Xplore in proceedings IEEE International Workshop on Biometrics and Forensics (IWBF), Salzburg, Austria, April 2022

Cited by in corpus (6)

• Towards minimizing efforts for Morphing Attacks -- Deep embeddings for morphing pair selection and improved Morphing Attack Detection
• Greedy-DiM: Greedy Algorithms for Unreasonably Effective Face Morphs
• SynMorph: Generating Synthetic Face Morphing Dataset with Mated Samples
• Fast-DiM: Towards Fast Diffusion Morphs
• The Impact of Print-Scanning in Heterogeneous Morph Evaluation Scenarios
• DOOMGAN:High-Fidelity Dynamic Identity Obfuscation Ocular Generative Morphing