Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data

• URL: http://arxiv.org/abs/2005.04871v2
• Date: Tue, 10 Nov 2020 03:54:26 GMT
• Title: Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data
• Authors: Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang
• Abstract summary: Black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. Black-box attacks often suffer from the issue of query inefficiency due to the high dimensionality of the input space. We propose a novel technique called the spanning attack, which constrains adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset.
• Score: 96.92837098305898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of pre-trained models. However, black-box attacks often suffer from the issue of query inefficiency due to the high dimensionality of the input space, and therefore incur a false sense of model robustness. In this paper, we relax the conditions of the black-box threat model, and propose a novel technique called the spanning attack. By constraining adversarial perturbations in a low-dimensional subspace via spanning an auxiliary unlabeled dataset, the spanning attack significantly improves the query efficiency of a wide variety of existing black-box attacks. Extensive experiments show that the proposed method works favorably in both soft-label and hard-label black-box attacks. Our code is available at https://github.com/wangwllu/spanning_attack.

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