Author:
Chen Pei,Feng Zhiyong,Xing Meng,Zhang Yiming,Zheng Jinqing
Publisher
Springer Nature Switzerland
Reference33 articles.
1. Agarwal, A., et al.: Crafting adversarial perturbations via transformed image component swapping. IEEE Trans. Image Process. 31, 7338–7349 (2022)
2. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium On Security and Privacy (sp), pp. 39–57. IEEE (2017)
3. Croce, F., Hein, M.: Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: International Conference On Machine Learning, pp. 2206–2216. PMLR (2020)
4. Das, N., et al.: Shield: Fast, practical defense and vaccination for deep learning using jpeg compression. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 196–204 (2018)
5. Dong, Y., et al.: Boosting adversarial attacks with momentum. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9185–9193 (2018)