Fighting fire with fire: A spatial-frequency ensemble relation network with generative adversarial learning for adversarial image classification
文献类型：期刊论文
| 作者 | Zheng, Wenbo2,3; Yan, Lan3,4 ; Gou, Chao1; Wang, Fei-Yue3
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| 刊名 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
 |
| 出版日期 | 2021-01-26 |
| 页码 | 41 |
| 关键词 | adversarial examples ensemble learning generative adversarial learning image classification meta-learning |
| ISSN号 | 0884-8173 |
| DOI | 10.1002/int.22372 |
| 通讯作者 | Wang, Fei-Yue(feiyue.wang@ia.ac.cn) |
| 英文摘要 | Adversarial images generated by generative adversarial networks are not close to any existing benign images, and contain nonrobust features that have been identified as critical to the robustness of a machine learning model. Since adversarial images have an underlying distribution that differs from normal images, these kinds of images can offer valuable features for training a robust model. To deal with these special features, we focus on a novel machine learning task of adversarial images classification, where adversarial images can be used to investigate the problem of classifying adversarial images themselves. In the setting of this novel task, adversarial images are the ONLY kind of data used in training and testing, rather than not just a set of testing images as usual. To this end, we propose a novel spatial-frequency ensemble relation network with generative adversarial learning. First, we present a spatial-frequency ensemble representation learning to extract the feature of training images. Second, we design a meta-learning-based relation model to gain the relationship between images. Third, to achieve a robust model, we utilize generative adversarial learning and transform the relationship into a Jacobian matrix. Finally, we design a discriminator model that determines whether an adversarial image is from the matching category or not. Experimental results demonstrate that our approach achieves significantly higher performance compared with other state-of-the-arts. |
| 资助项目 | National Key R&D Program of China[2018AAA0101502] ; National Key R&D Program of China[2020YFB1600400] ; Key Research and Development Program of Guangzhou[202007050002] ; National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[U1811463] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000611667200001 |
| 出版者 | WILEY |
| 资助机构 | National Key R&D Program of China ; Key Research and Development Program of Guangzhou ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42912]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Fei-Yue |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou, Peoples R China 2.Xi An Jiao Tong Univ, Sch Software Engn, Xian, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Wenbo,Yan, Lan,Gou, Chao,et al. Fighting fire with fire: A spatial-frequency ensemble relation network with generative adversarial learning for adversarial image classification[J]. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,2021:41. |
| APA | Zheng, Wenbo,Yan, Lan,Gou, Chao,&Wang, Fei-Yue.(2021).Fighting fire with fire: A spatial-frequency ensemble relation network with generative adversarial learning for adversarial image classification.INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS,41. |
| MLA | Zheng, Wenbo,et al."Fighting fire with fire: A spatial-frequency ensemble relation network with generative adversarial learning for adversarial image classification".INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2021):41. |
入库方式： OAI收割
来源：自动化研究所
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