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Article type: Research Article
Authors: Gao, Lijuna | Zhu, Jialonga; * | Zhang, Xuedonga | Wu, Jiehonga | Yin, Hangb
Affiliations: [a] Department of Computer Science and Technology, Shenyang Aerospace University, Shenyang, China | [b] College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
Correspondence: [*] Corresponding author. Jialong Zhu, Department of Computer Science and Technology, Shenyang Aerospace University, Shenyang 110136, China. E-mail: 516818773@qq.com.
Abstract: Deep neural networks have been extensively applied in fields such as image classification, object detection, and face recognition. However, research has shown that adversarial samples with subtle perturbations can effectively deceive these networks. Existing methods for generating such adversarial images often lack stealth and robustness. In this study, we present an enhanced attack strategy based on traditional Generative Adversarial Networks (GANs). We integrate image texture into the unsupervised training scheme, guiding the model to focus perturbations in high-texture areas. We also introduce a dynamic equilibrium training strategy that employs Differential Evolution algorithms to adaptively adjust both network weight parameters and the training ratio between the generator and discriminator, achieving a self-balancing training process. Further, we propose an image local optimization algorithm to eliminate perturbations in non-sensitive areas through weighted filtering. The model is validated using benchmark datasets such as MNIST, ImageNet and SVHN. Through extensive experimental evaluations, our approach shows a 4.93% improvement in attack success rate against conventional models and a 10.23% increase against defense models compared to state-of-the-art attack methods.
Keywords: Adversarial samples, texture sensitive region, GAN networks, micro parallax, optimization algorithm
DOI: 10.3233/JIFS-231653
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2573-2584, 2024
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