Abstract
Neural networks can be fooled by adversarial examples. Recently, many methods have been proposed to generate adversarial examples, but these works mainly concentrate on the pixel-wise information, which limits the transferability of adversarial examples. Different from these methods, we introduce perceptual module to extract the high-level representations and change the manifold of the adversarial examples. Besides, we propose a novel network structure to replace the generative adversarial network (GAN). The improved structure ensures high similarity of adversarial examples and promotes the stability of training process. Extensive experiments demonstrate that our method has significant improvement on the transferability. Furthermore, the adversarial training defence method is invalid for our attack.
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Acknowledgements
This work is supported by Guangdong Province Key Area R&D Program under grant No. 2018B010113001, the R&D Program of Shenzhen under grant No. JCYJ20170307153157440 and the Shenzhen Key Lab of Software Defined Networking under grant No. ZDSYS20140509172959989, and the National Natural Science Foundation of China under Grant No. 61802220.
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Hao, Y., Li, T., Li, L., Jiang, Y., Cheng, X. (2019). HLR: Generating Adversarial Examples by High-Level Representations. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_57
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DOI: https://doi.org/10.1007/978-3-030-30508-6_57
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