Adaptive Adversarial Logits Pairing
文献类型：期刊论文
| 作者 | Wu, Shangxi4; Sang, Jitao3,4 ; Xu, Kaiyan4; Zheng, Guanhua2; Xu, Changsheng1
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| 刊名 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 20期号:2页码:16 |
| ISSN号 | 1551-6857 |
| 关键词 | Adversarial defense adaptive dropout |
| DOI | 10.1145/3616375 |
| 通讯作者 | Wu, Shangxi(wushangxi@bjtu.edu.cn) |
| 英文摘要 | Adversarial examples provide an opportunity as well as impose a challenge for understanding image classification systems. Based on the analysis of the adversarial training solution-Adversarial Logits Pairing (ALP), we observed in this work that: (1) The inference of adversarially robust model tends to rely on fewer high-contribution features compared with vulnerable ones. (2) The training target of ALP does not fit well to a noticeable part of samples, where the logits pairing loss is overemphasized and obstructs minimizing the classification loss. Motivated by these observations, we design an Adaptive Adversarial Logits Pairing (AALP) solution by modifying the training process and training target of ALP. Specifically, AALP consists of an adaptive feature optimization module with Guided Dropout to systematically pursue fewer high-contribution features, and an adaptive sample weighting module by setting sample-specific training weights to balance between logits pairing loss and classification loss. The proposed AALP solution demonstrates superior defense performance on multiple datasets with extensive experiments. |
| WOS关键词 | NEURAL-NETWORKS ; ROBUSTNESS |
| 资助项目 | Fundamental Research Funds for the Central Universities[2023JBZY033] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[62172094] ; Beijing Natural Science Foundation[JQ20023] ; CCF-Zhipu AI Large Model Fund |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | ASSOC COMPUTING MACHINERY |
| WOS记录号 | WOS:001092595800026 |
| 资助机构 | Fundamental Research Funds for the Central Universities ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; CCF-Zhipu AI Large Model Fund |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54421]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Wu, Shangxi |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Univ Sci & Technol China, Beijing, Peoples R China 3.Tianjin Normal Univ, Tianjin, Peoples R China 4.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Shangxi,Sang, Jitao,Xu, Kaiyan,et al. Adaptive Adversarial Logits Pairing[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2024,20(2):16. |
| APA | Wu, Shangxi,Sang, Jitao,Xu, Kaiyan,Zheng, Guanhua,&Xu, Changsheng.(2024).Adaptive Adversarial Logits Pairing.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,20(2),16. |
| MLA | Wu, Shangxi,et al."Adaptive Adversarial Logits Pairing".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 20.2(2024):16. |
入库方式： OAI收割
来源：自动化研究所
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