{"title":"VTFR-AT:具有视觉变换和特征鲁棒性的对抗训练","authors":"Xiang Li;Changfei Zhao;Xinyang Deng;Wen Jiang","doi":"10.1109/TETCI.2024.3370004","DOIUrl":null,"url":null,"abstract":"Research on the robustness of deep neural networks to adversarial samples has grown rapidly since studies have shown that deep learning is susceptible to adversarial perturbation noise. Adversarial training is widely regarded as the most powerful defence strategy against adversarial attacks out of many defence strategies. It has been shown that the adversarial vulnerability of models is due to the learned non-robust feature in the data. However, few methods have attempted to improve adversarial training by enhancing the critical information in the data, i.e., the important region of the object. Moreover, adversarial training is prone to overfitting the model due to the overuse of training set samples. In this paper, we propose a new adversarial training framework with visual transformation and feature robustness, named VTFR-AT. The visual transformation (VT) module enhances principal information in images, weakens background information, and eliminates nuisance noise by pre-processing images. The feature robustness (FR) loss function enhances the network feature extraction partly against perturbation by constraining the feature similarity of the network on similar images. Extensive experiments have shown that the VTFR framework can substantially promote the performance of models on adversarial samples and improve the adversarial robustness and generalization capabilities. As a plug-and-play module, the proposed framework can be easily combined with various existing adversarial training methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"VTFR-AT: Adversarial Training With Visual Transformation and Feature Robustness\",\"authors\":\"Xiang Li;Changfei Zhao;Xinyang Deng;Wen Jiang\",\"doi\":\"10.1109/TETCI.2024.3370004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research on the robustness of deep neural networks to adversarial samples has grown rapidly since studies have shown that deep learning is susceptible to adversarial perturbation noise. Adversarial training is widely regarded as the most powerful defence strategy against adversarial attacks out of many defence strategies. It has been shown that the adversarial vulnerability of models is due to the learned non-robust feature in the data. However, few methods have attempted to improve adversarial training by enhancing the critical information in the data, i.e., the important region of the object. Moreover, adversarial training is prone to overfitting the model due to the overuse of training set samples. In this paper, we propose a new adversarial training framework with visual transformation and feature robustness, named VTFR-AT. The visual transformation (VT) module enhances principal information in images, weakens background information, and eliminates nuisance noise by pre-processing images. The feature robustness (FR) loss function enhances the network feature extraction partly against perturbation by constraining the feature similarity of the network on similar images. Extensive experiments have shown that the VTFR framework can substantially promote the performance of models on adversarial samples and improve the adversarial robustness and generalization capabilities. As a plug-and-play module, the proposed framework can be easily combined with various existing adversarial training methods.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-03-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10474405/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10474405/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
VTFR-AT: Adversarial Training With Visual Transformation and Feature Robustness
Research on the robustness of deep neural networks to adversarial samples has grown rapidly since studies have shown that deep learning is susceptible to adversarial perturbation noise. Adversarial training is widely regarded as the most powerful defence strategy against adversarial attacks out of many defence strategies. It has been shown that the adversarial vulnerability of models is due to the learned non-robust feature in the data. However, few methods have attempted to improve adversarial training by enhancing the critical information in the data, i.e., the important region of the object. Moreover, adversarial training is prone to overfitting the model due to the overuse of training set samples. In this paper, we propose a new adversarial training framework with visual transformation and feature robustness, named VTFR-AT. The visual transformation (VT) module enhances principal information in images, weakens background information, and eliminates nuisance noise by pre-processing images. The feature robustness (FR) loss function enhances the network feature extraction partly against perturbation by constraining the feature similarity of the network on similar images. Extensive experiments have shown that the VTFR framework can substantially promote the performance of models on adversarial samples and improve the adversarial robustness and generalization capabilities. As a plug-and-play module, the proposed framework can be easily combined with various existing adversarial training methods.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.