Yang Wei, Zhuo Ma, Zhuo Ma, Zhan Qin, Yang Liu, Bin Xiao, Xiuli Bi, Jianfeng Ma
{"title":"有效提高无数据黑盒攻击替代训练的数据多样性","authors":"Yang Wei, Zhuo Ma, Zhuo Ma, Zhan Qin, Yang Liu, Bin Xiao, Xiuli Bi, Jianfeng Ma","doi":"10.1109/TDSC.2023.3347753","DOIUrl":null,"url":null,"abstract":"Recent substitute training methods have utilized the concept of Generative Adversarial Networks (GANs) to implement data-free black-box attacks. Specifically, in designing the generators, the substitute training methods use a similar structure to the generators in GANs. However, this design approach ignores the potential situation that the generators in GANs operate under real data supervision, while the generators in substitute training methods lack such supervision. This difference in data-supervised conditions constrain the diversity of data generated by the substitute training methods, resulting in inadequate data to support effective training of the substitute model. This impacts the substitute model's ability to attack the target model further. Consequently, to solve the above issues, we propose three strategies to improve the attack success rates. For the generator, we first propose a dense projection space that projects the input noise into various latent feature spaces to diversify feature information. Then, we introduce a novel disguised natural color mode. This mode improves information exchange between the generator's output layer and previous layers, allowing for more diverse generated data. Besides, we present a regularization method for the substitute model, called noise-based balanced learning, to prevent the potential risk of overfitting due to the lack of diversity of the generated data. In the experimental analysis, extensive experiments are conducted to validate the effectiveness of these proposed strategies.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effectively Improving Data Diversity of Substitute Training for Data-Free Black-Box Attack\",\"authors\":\"Yang Wei, Zhuo Ma, Zhuo Ma, Zhan Qin, Yang Liu, Bin Xiao, Xiuli Bi, Jianfeng Ma\",\"doi\":\"10.1109/TDSC.2023.3347753\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent substitute training methods have utilized the concept of Generative Adversarial Networks (GANs) to implement data-free black-box attacks. Specifically, in designing the generators, the substitute training methods use a similar structure to the generators in GANs. However, this design approach ignores the potential situation that the generators in GANs operate under real data supervision, while the generators in substitute training methods lack such supervision. This difference in data-supervised conditions constrain the diversity of data generated by the substitute training methods, resulting in inadequate data to support effective training of the substitute model. This impacts the substitute model's ability to attack the target model further. Consequently, to solve the above issues, we propose three strategies to improve the attack success rates. For the generator, we first propose a dense projection space that projects the input noise into various latent feature spaces to diversify feature information. Then, we introduce a novel disguised natural color mode. This mode improves information exchange between the generator's output layer and previous layers, allowing for more diverse generated data. Besides, we present a regularization method for the substitute model, called noise-based balanced learning, to prevent the potential risk of overfitting due to the lack of diversity of the generated data. 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Effectively Improving Data Diversity of Substitute Training for Data-Free Black-Box Attack
Recent substitute training methods have utilized the concept of Generative Adversarial Networks (GANs) to implement data-free black-box attacks. Specifically, in designing the generators, the substitute training methods use a similar structure to the generators in GANs. However, this design approach ignores the potential situation that the generators in GANs operate under real data supervision, while the generators in substitute training methods lack such supervision. This difference in data-supervised conditions constrain the diversity of data generated by the substitute training methods, resulting in inadequate data to support effective training of the substitute model. This impacts the substitute model's ability to attack the target model further. Consequently, to solve the above issues, we propose three strategies to improve the attack success rates. For the generator, we first propose a dense projection space that projects the input noise into various latent feature spaces to diversify feature information. Then, we introduce a novel disguised natural color mode. This mode improves information exchange between the generator's output layer and previous layers, allowing for more diverse generated data. Besides, we present a regularization method for the substitute model, called noise-based balanced learning, to prevent the potential risk of overfitting due to the lack of diversity of the generated data. In the experimental analysis, extensive experiments are conducted to validate the effectiveness of these proposed strategies.
期刊介绍:
The "IEEE Transactions on Dependable and Secure Computing (TDSC)" is a prestigious journal that publishes high-quality, peer-reviewed research in the field of computer science, specifically targeting the development of dependable and secure computing systems and networks. This journal is dedicated to exploring the fundamental principles, methodologies, and mechanisms that enable the design, modeling, and evaluation of systems that meet the required levels of reliability, security, and performance.
The scope of TDSC includes research on measurement, modeling, and simulation techniques that contribute to the understanding and improvement of system performance under various constraints. It also covers the foundations necessary for the joint evaluation, verification, and design of systems that balance performance, security, and dependability.
By publishing archival research results, TDSC aims to provide a valuable resource for researchers, engineers, and practitioners working in the areas of cybersecurity, fault tolerance, and system reliability. The journal's focus on cutting-edge research ensures that it remains at the forefront of advancements in the field, promoting the development of technologies that are critical for the functioning of modern, complex systems.