high-performance deep learning library. CoRR, abs/1912.01703, 2019. URL http://arxiv.org/abs/1912.01703.
[25] Andrea Paudice, Luis Muñoz-González, András György, and Emil C. Lupu. Detection of adversarial training examples in poisoning attacks through anomaly detection. CoRR, abs/1802.03041, 2018. URL http://arxiv.org/abs/1802.03041.
[26] Keivan Rezaei, Kiarash Banihashem, Atoosa Malemir Chegini, and Soheil Feizi. Run-off election: Improved provable defense against data poisoning attacks. CoRR, abs/2302.02300, 2023. doi: 10.48550/arXiv.2302.02300. URL https://doi.org/10.48550/arXiv.2302.02300.
[27] Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, and J. Zico Kolter. Certified robustness to label-flipping attacks via randomized smoothing. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, volume 119 of Proceedings of Machine Learning Research, pages 8230-8241. PMLR, 2020. URL http://proceedings.mlr.press/v119/rosenfeld20b.html.
[28] Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, and Nina Taft. Antidote: understanding and defending against poisoning of anomaly detectors. In Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, pages 1-14, 2009.
[29] Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. Hidden trigger backdoor attacks. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020, pages 11957-11965. AAAI Press, 2020. URL https://aaai.org/ojs/index.php/AAAI/article/view/6871.
[30] Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein. Poison frogs! targeted clean-label poisoning attacks on neural networks. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 6106-6116, 2018. URL https://proceedings.neurips.cc/paper/2018/black/22722a343513ed45f14905eb07621686-AAbstract.html.
[31] Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, and Ben Y Zhao. Poison forensics: Traceback of data poisoning attacks in neural networks. In 31st USENIX Security Symposium (USENIX Security 22), pages 3575-3592, 2022.
[32] Hossein Souri, Micah Goldblum, Liam Fowl, Rama Chellappa, and Tom Goldstein. Sleeper agent: Scalable hidden trigger backdoors for neural networks trained from scratch. CoRR, abs/2106.08970, 2021. URL https://arxiv.org/abs/2106.08970.
[33] Jacob Steinhardt, Pang Wei Koh, and Percy Liang. Certified defenses for data poisoning attacks. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 3517-3529, 2017. URL https://proceedings.neurips.cc/paper/2017/hash/9d7311ba459f9e45ed746755a32dcd11-Abtract.html.
[34] Brandon Tran, Jerry Li, and Aleksander Madry. Spectral signatures in backdoor attacks. In Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, and Roman Garnett, editors, Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 8011-8021, 2018. URL https://proceedings.neurips.cc/paper/2018/hash/280cf18baf4311c92aa5a042336587d3-AAbstract.html.
[35] Alexander Turner, Dimitris Tsipras, and Aleksander Madry. Label-consistent backdoor attacks. CoRR, abs/1912.02771, 2019. URL http://arxiv.org/abs/1912.02771.