A Multi-task Adversarial Attack Against Face Authentication
This work addresses security vulnerabilities in identity management systems for users and developers, though it is incremental as it extends existing attack methods to multi-task scenarios.
The authors tackled the problem of deep-learning-based face authentication systems being vulnerable to adversarial attacks, proposing MTADV, a multi-task adversarial attack algorithm that is adaptable for multiple users or systems and effective across various datasets and models, achieving high success rates in white- and gray-box settings.
Deep-learning-based identity management systems, such as face authentication systems, are vulnerable to adversarial attacks. However, existing attacks are typically designed for single-task purposes, which means they are tailored to exploit vulnerabilities unique to the individual target rather than being adaptable for multiple users or systems. This limitation makes them unsuitable for certain attack scenarios, such as morphing, universal, transferable, and counter attacks. In this paper, we propose a multi-task adversarial attack algorithm called MTADV that are adaptable for multiple users or systems. By interpreting these scenarios as multi-task attacks, MTADV is applicable to both single- and multi-task attacks, and feasible in the white- and gray-box settings. Furthermore, MTADV is effective against various face datasets, including LFW, CelebA, and CelebA-HQ, and can work with different deep learning models, such as FaceNet, InsightFace, and CurricularFace. Importantly, MTADV retains its feasibility as a single-task attack targeting a single user/system. To the best of our knowledge, MTADV is the first adversarial attack method that can target all of the aforementioned scenarios in one algorithm.