“We Must Protect the Transformers”: Understanding Efficacy of Backdoor Attack Mitigation on Transformer Models

Author:

Raj RohitORCID,Roy BiplabORCID,Das AbirORCID,Mondal MainackORCID

Publisher

Springer Nature Switzerland

Reference36 articles.

1. Qiu, H., et al.: Towards a critical evaluation of robustness for deep learning backdoor countermeasures (2022). arXiv:abs/2204.06273

2. Wang, B., et al.: Neural cleanse: identifying and mitigating backdoor attacks in neural networks. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 707–723 (2019)

3. Gao, Y., et al.: Backdoor attacks and countermeasures on deep learning: a comprehensive review (2020). arXiv:abs/2007.10760

4. Liu, Y., Xie, Y., Srivastava, A.: Neural trojans. In: 2017 IEEE International Conference on Computer Design (ICCD), pp. 45–48 (2017)

5. Gu, T., Dolan-Gavitt, B., Garg, S.: BadNets: identifying vulnerabilities in the machine learning model supply chain. arXiv preprint: arXiv:1708.06733 (2017)

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