Abstract
In the field of artificial intelligence security, adversarial machine learning has made breakthroughs. However, it is still vulnerable to attacks under a wide variety of adversarial samples, and adversarial training is a very effective method against a wide variety of adversarial sample attacks. However, adversarial training tends to improve the accuracy of the adversarial samples while reducing the accuracy of the original samples. Thus, the robustness of adversarial training is greatly reduced. In order to improve the robustness of adversarial training, this paper proposes a fusion adversarial training model based on Generate adversarial network (GAN), which is applied to the adversarial training process, and this unsupervised learning framework can better learn the distribution of samples to generate high-quality samples. By controlling the proportion of different training losses, we improve the classification accuracy of the adversarial training model while maintaining the relatively high accuracy of the model for the original samples, thus greatly improving the robustness of the adversarial training model. In this paper, we conduct experiments based on the Fashion-MNIST and CIFAR10 datasets, and the results show that our model has high accuracy on both the original and adversarial samples for both grayscale and color maps.
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Cao, Y., Lin, Y., Ning, S., Pi, H., Zhang, J., Hu, J. (2022). GAN-Based Fusion Adversarial Training. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13370. Springer, Cham. https://doi.org/10.1007/978-3-031-10989-8_5
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