On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations

• URL: http://arxiv.org/abs/2203.12387v1
• Date: Wed, 23 Mar 2022 13:02:41 GMT
• Title: On the (Limited) Generalization of MasterFace Attacks and Its Relation to the Capacity of Face Representations
• Authors: Philipp Terh\"orst, Florian Bierbaum, Marco Huber, Naser Damer, Florian Kirchbuchner, Kiran Raja, Arjan Kuijper
• Abstract summary: We study the generalizability of MasterFace attacks in empirical and theoretical investigations. We estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. We conclude that MasterFaces should not be seen as a threat to face recognition systems but as a tool to enhance the robustness of face recognition models.
• Score: 11.924504853735645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A MasterFace is a face image that can successfully match against a large portion of the population. Since their generation does not require access to the information of the enrolled subjects, MasterFace attacks represent a potential security risk for widely-used face recognition systems. Previous works proposed methods for generating such images and demonstrated that these attacks can strongly compromise face recognition. However, previous works followed evaluation settings consisting of older recognition models, limited cross-dataset and cross-model evaluations, and the use of low-scale testing data. This makes it hard to state the generalizability of these attacks. In this work, we comprehensively analyse the generalizability of MasterFace attacks in empirical and theoretical investigations. The empirical investigations include the use of six state-of-the-art FR models, cross-dataset and cross-model evaluation protocols, and utilizing testing datasets of significantly higher size and variance. The results indicate a low generalizability when MasterFaces are training on a different face recognition model than the one used for testing. In these cases, the attack performance is similar to zero-effort imposter attacks. In the theoretical investigations, we define and estimate the face capacity and the maximum MasterFace coverage under the assumption that identities in the face space are well separated. The current trend of increasing the fairness and generalizability in face recognition indicates that the vulnerability of future systems might further decrease. We conclude that MasterFaces should not be seen as a threat to face recognition systems but, on the contrary, seen as a tool to understand and enhance the robustness of face recognition models.

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