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Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
905-910

Combining adversarial training and feature mixing for siamese network defense models

Zhang Xinjun
Cheng Yuqing
School of Electronic & Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0318

Abstract

Neural network models are vulnerable to adversarial sample attacks. Aiming at the problem that current defense methods focus on improving the model structure or the model only uses the adversarial training method which leads to a single type of defense and impairs the models classification ability and inefficiency, this paper proposed the method of combining the adversarial training and the feature mixture to train the siamese neural network model(SS-ResNet18). The method mixed the training set sample data by linear interpolation, built a siamese network model using the residual attention module, and inputted PGD antagonistic samples and normal samples into different branches of the network for training. It interchanged the input features in the feature space between neighboring sample parts to enhance the networks immunity to interference, combining the adversarial loss and the classification loss as the overall loss function of the network and smoothing it with labels. Experimented on CIFAR-10 and SVHN datasets, the method shows excellent defense performance under white-box attack, and the success rate of the models defense against antagonistic samples, such as PGD, JSMA, etc., under black-box attack is more than 80%. At the same time, the SS-ResNet18 model time spent is only one-half of the one-half of the subspace antagonistic training method. The experimental results show that the SS-ResNet18 model can defend against a variety of adversarial sample attacks, and is robust and less time-consuming to train compared to existing defense methods.

Foundation Support

2022年辽宁省教育厅基本科研项目(LJKMZ20220678)
辽宁省教育厅科学研究经费项目(LJ2020JCL007)
辽宁工程技术大学博士启动基金资助项目(20-1020)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0318
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Technology of Information Security
Pages: 905-910
Serial Number: 1001-3695(2024)03-039-0905-06

Publish History

[2023-09-19] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

张新君, 程雨晴. 结合对抗训练和特征混合的孪生网络防御模型 [J]. 计算机应用研究, 2024, 41 (3): 905-910. (Zhang Xinjun, Cheng Yuqing. Combining adversarial training and feature mixing for siamese network defense models [J]. Application Research of Computers, 2024, 41 (3): 905-910. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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