Abstract
The existence of adversarial samples seriously threatens the security of various deep learning models. Therefore, the detection of adversarial examples is a very important work. Motivated by the comparison with feature maps of adversarial examples and normal examples, we designed an autoencoder to detect the adversarial examples using the feature maps. The feature autoencoder has been evaluated to detect FGSM, DeepFool, JSMA and C&W attacks on CIFAR-10 datasets. The experimental results showed that feature-level detector can detect state-of-art attacks more effectively than at the pixel-level.
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Acknowledgement
This work is supported by the Project of Educational Commission of Guangdong Province of China (2018GKTSCX114), Key Research and Development Program of Hainan Province (Grant No. ZDYF2020033), Young Talents’ Science and Technology Innovation Project of Hainan Association for Science and Technology (Grant No. QCXM202007), Hainan Provincial Natural Science Foundation of China (Grant No. 621RC612), Hainan Provincial Natural Science Foundation of China (Grant No. 2019RC107).
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Ye, H., Liu, X., Yan, A., Li, L., Li, X. (2022). Detect Adversarial Examples by Using Feature Autoencoder. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13340. Springer, Cham. https://doi.org/10.1007/978-3-031-06791-4_19
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