mlsanityData to 7 Oct 2026

CE-based white-box adversarial attacks will not work using super-fitting

arXiv 2205.02741 ↗First posted 4 May 2022cs.LG cs.AI cs.CRcited by 0

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Abstract (arXiv)

Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation to the input will make the model obtain wrong results. This is especially dangerous for some systems with high-security requirements, so this paper proposes a new defense method by using the model super-fitting state to improve the model's adversarial robustness (i.e., the accuracy under adversarial attacks). This paper mathematically proves the effectiveness of super-fitting and enables the model to reach this state quickly by minimizing unrelated category scores (MUCS). Theoretically, super-fitting can resist any existing (even future) CE-based white-box adversarial attacks. In addition, this paper uses a variety of powerful attack algorithms to evaluate the adversarial robustness of super-fitting, and the proposed method is compared with nearly 50 defense models from recent conferences. The experimental results show that the super-fitting method in this paper can make the trained model obtain the highest adversarial robustness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 49 references that are in the map. First posted on arXiv 4 May 2022.

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37 of 49 references.

References · 37

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25 more
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