Boosting Decision-based Black-box Adversarial Attacks with Random Sign Flip | |
Weilun, Chen1,2; Zhaoxiang, Zhang1,2,3; Xiaolin, Hu4; Baoyuan, Wu5,6 | |
2020-08 | |
会议名称 | European Conference on Computer Vision |
会议日期 | 2020-8 |
会议地点 | UK |
摘要 | Decision-based black-box adversarial attacks (decision-based attack) pose a severe threat to current deep neural networks, as they only need the predicted label of the target model to craft adversarial examples. However, existing decision-based attacks perform poorly on the $ l_\infty $ setting and the required enormous queries cast a shadow over the practicality. In this paper, we show that just randomly flipping the signs of a small number of entries in adversarial perturbations can significantly boost the attack performance. We name this simple and highly efficient decision-based $ l_\infty $ attack as Sign Flip Attack. Extensive experiments on CIFAR-10 and ImageNet show that the proposed method outperforms existing decision-based attacks by large margins and can serve as a strong baseline to evaluate the robustness of defensive models. We further demonstrate the applicability of the proposed method on real-world systems. |
七大方向——子方向分类 | 生物特征识别 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44323 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Zhaoxiang, Zhang |
作者单位 | 1.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) 2.Center for Excellence in Brain Science and Intelligence Technology, CAS 3.Tsinghua University 4.The Chinese University of Hong Kong, Shenzhen 5.Tencent AI Lab 6.School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Weilun, Chen,Zhaoxiang, Zhang,Xiaolin, Hu,et al. Boosting Decision-based Black-box Adversarial Attacks with Random Sign Flip[C],2020. |
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SFA_eccv2020.pdf(1212KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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