Backdoor Attacks Leveraging Latent Representation in Competitive Learning

Author:

Iwahana KazukiORCID,Yanai NaotoORCID,Fujiwara Toru

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Chen, B., et al.: Detecting backdoor attacks on deep neural networks by activation clustering. In: Proceedings of SafeAI 2019 (2019)

2. Doan, K., Lao, Y., Li, P.: Backdoor attack with imperceptible input and latent modification. In: Proceedings of NeurIPS 2021. vol. 34, pp. 18944–18957. Curran Associates, Inc. (2021). https://proceedings.neurips.cc/paper/2021/file/9d99197e2ebf03fc388d09f1e94af89b-Paper.pdf

3. Gao, Y., Xu, C., Wang, D., Chen, S., Ranasinghe, D.C., Nepal, S.: Strip: A defence against trojan attacks on deep neural networks. In: Proceedings of ACSAC 2019, pp. 113–125. ACM (2019)

4. Gu, T., Liu, K., Dolan-Gavitt, B., Garg, S.: BadNets: evaluating backdooring attacks on deep neural networks. IEEE Access 7, 47230–47244 (2019)

5. Hampel, F.R.: The influence curve and its role in robust estimation. J. Am. Stat. Assoc. 69(346), 383–393 (1974)

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