Model-Contrastive Learning for Backdoor Elimination

Author:

Yue Zhihao1ORCID,Xia Jun1ORCID,Ling Zhiwei1ORCID,Hu Ming1ORCID,Wang Ting1ORCID,Wei Xian1ORCID,Chen Mingsong1ORCID

Affiliation:

1. East China Normal University, Shanghai, China

Funder

Shanghai Trusted Industry Internet Software Collaborative Innovation Center

Natural Science Foundation of China

Digital Silk Road Shanghai International Joint Lab of Trustworthy Intelligent Software

Publisher

ACM

Reference59 articles.

1. A New Backdoor Attack in CNNS by Training Set Corruption Without Label Poisoning

2. Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff

3. Nicholas Carlini and David A. Wagner . 2017. Towards Evaluating the Robustness of Neural Networks . In Proceedings of the IEEE Symposium on Security and Privacy (S&P). 39--57 . Nicholas Carlini and David A. Wagner. 2017. Towards Evaluating the Robustness of Neural Networks. In Proceedings of the IEEE Symposium on Security and Privacy (S&P). 39--57.

4. Ting Chen , Simon Kornblith , Mohammad Norouzi , and Geoffrey Hinton . 2020 a. A simple framework for contrastive learning of visual representations . In Proceedings of the International Conference on Machine Learning (ICML). 1597--1607 . Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020a. A simple framework for contrastive learning of visual representations. In Proceedings of the International Conference on Machine Learning (ICML). 1597--1607.

5. Ting Chen , Simon Kornblith , Kevin Swersky , Mohammad Norouzi , and Geoffrey E Hinton . 2020 b. Big self-supervised models are strong semi-supervised learners . In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 22243--22255 . Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. 2020b. Big self-supervised models are strong semi-supervised learners. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS). 22243--22255.

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