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Research on generative adversarial attacks under black-box conditions
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Zhaoyang ZHANG1, Fanghui SUN1, Mingxu ZHANG2, Wei SONG3, Zhenbang WANG4, Yingqi WANG1, Keqing ZHANG1, Shen WANG1
Information Countermeasure Technology | 2025, 4(5) : 1 - 21
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Information Countermeasure Technology | 2025, 4(5): 1-21
Research Articles
Research on generative adversarial attacks under black-box conditions
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Zhaoyang ZHANG1, Fanghui SUN1, Mingxu ZHANG2, Wei SONG3, Zhenbang WANG4, Yingqi WANG1, Keqing ZHANG1, Shen WANG1
Affiliations
  • 1School of Cybersecurity, Harbin Institute of Technology, Harbin 150001, China
  • 2China Electronics Society, Beijing 100036, China
  • 3China Mobile IoT Co., Ltd., Chongqing 401336, China
  • 4State Grid Heilongjiang Electric Power Co., Ltd., Harbin 150090, China
doi: 10.12399/j.issn.2097-163x.2025.05.001
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In the context of image adversarial attacks,white-box attacks targeting the target model often yield the best performance. However,in practice,it is usually difficult to obtain the architecture of the target model,which makes improving the transferability of adversarial examples particularly crucial. To address this issue,a training method based on generative adversarial network(GAN)was proposed to generate adversarial examples with strong transferability.The study finds that images themselves possess model-agnostic vulnerabilities,and generative methods implement attacks precisely by exploiting this characteristic. Unlike traditional methods that perform fine-tuning within the neighborhood of the original image,this method generates images with maximum likelihood from the distribution of other categories. These images are visually close to real images but can effectively mislead classifiers. During the training process,the generator produces adversarial examples,while the discriminator judges the correctness of their labels. The two components optimize collaboratively,continuously enhancing the adversarial potency and authenticity of the examples. Experiments show that the attack success rate of generative adversarial examples on multiple models is significantly higher than that of traditional methods,with an average improvement of approximately 25%,demonstrating stronger cross-model generalization ability. This result indicates that generative adversarial attacks not only enhance the practicality of black-box attacks but also reveal the widespread vulnerabilities of deep models,providing directions for the design of subsequent defense mechanisms.

generative adversarial attack  /  model transferability  /  black-box attack
Zhaoyang ZHANG, Fanghui SUN, Mingxu ZHANG, Wei SONG, Zhenbang WANG, Yingqi WANG, Keqing ZHANG, Shen WANG. Research on generative adversarial attacks under black-box conditions[J]. Information Countermeasure Technology, 2025 , 4 (5) : 1 -21 . DOI: 10.12399/j.issn.2097-163x.2025.05.001
Year 2025 volume 4 Issue 5
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doi: 10.12399/j.issn.2097-163x.2025.05.001
  • Receive Date:2025-07-07
  • Online Date:2026-04-23
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  • Received:2025-07-07
  • Revised:2025-08-08
Affiliations
    1School of Cybersecurity, Harbin Institute of Technology, Harbin 150001, China
    2China Electronics Society, Beijing 100036, China
    3China Mobile IoT Co., Ltd., Chongqing 401336, China
    4State Grid Heilongjiang Electric Power Co., Ltd., Harbin 150090, China
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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