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arXiv · 1910.00511

An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack

Yang Zhang·Shiyu Chang·Mo Yu·Kaizhi Qian
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Abstract

There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level. The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed to cause mis-classification, also known as the margin of an input feature. While the former paradigm is well-resolved, the latter is not. Existing zero-confidence attacks either introduce significant ap-proximation errors, or are too time-consuming. We therefore propose MARGINATTACK, a zero-confidence attack framework that is able to compute the margin with improved accuracy and efficiency. Our experiments show that MARGINATTACK is able to compute a smaller margin than the state-of-the-art zero-confidence attacks, and matches the state-of-the-art fix-perturbation at-tacks. In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most ac-curate zero-confidence attack algorithm.

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Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian. 2019-10-01. An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack. https://arxiv.org/abs/1910.00511

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