Exploring Effective Data for Surrogate Training Towards Black-box Attack

Xuxiang Sun, Gong Cheng, Hongda Li, Lei Pei, Junwei Han

科研成果: 书/报告/会议事项章节会议稿件同行评审

23 引用 (Scopus)

摘要

Without access to the training data where a black-box victim model is deployed, training a surrogate model for black-box adversarial attack is still a struggle. In terms of data, we mainly identify three key measures for effective surrogate training in this paper. First, we show that leveraging the loss introduced in this paper to enlarge the inter-class similarity makes more sense than enlarging the inter-class diversity like existing methods. Next, unlike the approaches that expand the intra-class diversity in an implicit model-agnostic fashion, we propose a loss function specific to the surrogate model for our generator to enhance the intra-class diversity. Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training. To this end, we propose a triple-player framework by introducing a discriminator into the traditional data-free framework. In this way, our method can be competitive when there are few semantic overlaps between the scarce proxy data (with the size between 1 k and 5k) and the training data. We evaluate our method on a range of victim models and datasets. The extensive results witness the effectiveness of our method. Our source code is available at https://github.com/xuxiangsun/ST-Data.

源语言英语
主期刊名Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
出版商IEEE Computer Society
15334-15343
页数10
ISBN(电子版)9781665469463
DOI
出版状态已出版 - 2022
活动2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, 美国
期限: 19 6月 202224 6月 2022

出版系列

姓名Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2022-June
ISSN(印刷版)1063-6919

会议

会议2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
国家/地区美国
New Orleans
时期19/06/2224/06/22

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