An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack

Yang Zhang, Shiyu Chang, Mo Yu, Kaizhi Qian

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 misclassification, 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 approximation 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 attacks. In addition, it runs significantly faster than the Carlini-Wagner attack, currently the most accurate zero-confidence attack algorithm.

Keywords: adversarial attack, zero-confidence attack

TL;DR: This paper introduces MarginAttack, a stronger and faster zero-confidence adversarial attack.

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/an-efficient-and-margin-approaching-zero/code)