Learning black-box attackers with transferable priors and query feedback
Advances in Neural Information Processing Systems, 2020•proceedings.neurips.cc
This paper addresses the challenging black-box adversarial attack problem, where only
classification confidence of a victim model is available. Inspired by consistency of visual
saliency between different vision models, a surrogate model is expected to improve the
attack performance via transferability. By combining transferability-based and query-based
black-box attack, we propose a surprisingly simple baseline approach (named SimBA++)
using the surrogate model, which significantly outperforms several state-of-the-art methods …
classification confidence of a victim model is available. Inspired by consistency of visual
saliency between different vision models, a surrogate model is expected to improve the
attack performance via transferability. By combining transferability-based and query-based
black-box attack, we propose a surprisingly simple baseline approach (named SimBA++)
using the surrogate model, which significantly outperforms several state-of-the-art methods …
Abstract
This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available. Inspired by consistency of visual saliency between different vision models, a surrogate model is expected to improve the attack performance via transferability. By combining transferability-based and query-based black-box attack, we propose a surprisingly simple baseline approach (named SimBA++) using the surrogate model, which significantly outperforms several state-of-the-art methods. Moreover, to efficiently utilize the query feedback, we update the surrogate model in a novel learning scheme, named High-Order Gradient Approximation (HOGA). By constructing a high-order gradient computation graph, we update the surrogate model to approximate the victim model in both forward and backward pass. The SimBA++ and HOGA result in Learnable Black-Box Attack (LeBA), which surpasses previous state of the art by considerable margins: the proposed LeBA significantly reduces queries, while keeping higher attack success rates close to 100% in extensive ImageNet experiments, including attacking vision benchmarks and defensive models. Code is open source at https://github. com/TrustworthyDL/LeBA.
proceedings.neurips.cc
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