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Poster
Improving Black-box Adversarial Attacks with a Transfer-based Prior
Shuyu Cheng · Yinpeng Dong · Tianyu Pang · Hang Su · Jun Zhu

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #16
in Algorithms -- Adversarial Learning »

We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients. Previous methods tried to approximate the gradient either by using a transfer gradient of a surrogate white-box model, or based on the query feedback. However, these methods often suffer from low attack success rates or poor query efficiency since it is non-trivial to estimate the gradient in a high-dimensional space with limited information. To address these problems, we propose a prior-guided random gradient-free (P-RGF) method to improve black-box adversarial attacks, which takes the advantage of a transfer-based prior and the query information simultaneously. The transfer-based prior given by the gradient of a surrogate model is appropriately integrated into our algorithm by an optimal coefficient derived by a theoretical analysis. Extensive experiments demonstrate that our method requires much fewer queries to attack black-box models with higher success rates compared with the alternative state-of-the-art methods.

Keywords: Privacy, Anonymity, and Security · Applications · Algorithms · Adversarial Learning
[ Paper]  [ Slides] 

Author Information

Shuyu Cheng (Tsinghua University)
Yinpeng Dong (Tsinghua University)
Tianyu Pang (Tsinghua University)
Hang Su (Tsinghua Univiersity)
Jun Zhu (Tsinghua University)

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