Guanhong Tao

@inproceedings{tao2023distribution,
  title = {Distribution Preserving Backdoor Attack in Self-supervised Learning},
  author = {Tao*, Guanhong and Wang*, Zhenting and Feng, Shiwei and Shen, Guangyu and Ma, Shiqing and Zhang, Xiangyu},
  booktitle = {Proceedings of the 45th IEEE Symposium on Security and Privacy},
  year = {2024},
  location = {San Francisco, CA, USA},
  organization = {IEEE Computer Society},
}

@inproceedings{zhang2024large,
  title = {On Large Language Models’ Resilience to Coercive Interrogation},
  author = {Zhang, Zhuo and Shen, Guangyu and Tao, Guanhong and Cheng, Siyuan and Zhang, Xiangyu},
  booktitle = {Proceedings of the 45th IEEE Symposium on Security and Privacy},
  pages = {252--252},
  year = {2024},
  location = {San Francisco, CA, USA},
  organization = {IEEE Computer Society},
}

@inproceedings{tao2023hard,
  author = {Tao, Guanhong and An, Shengwei and Cheng, Siyuan and Shen, Guangyu and Zhang, Xiangyu},
  title = {Hard-label Black-box Universal Adversarial Patch Attack},
  booktitle = {Proceedings of the 32nd USENIX Security Symposium},
  year = {2023},
  location = {Anaheim, CA, USA},
  isbn = {978-1-939133-37-3},
  pages = {697--714},
  publisher = {USENIX Association},
}

@inproceedings{sun2023backdooring,
  title = {Backdooring Neural Code Search},
  author = {Sun*, Weisong and Chen*, Yuchen and Tao*, Guanhong and Fang, Chunrong and Zhang, Xiangyu and Zhang, Quanjun and Luo, Bin},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics},
  year = {2023},
  location = {Toronto, Canada},
}

@inproceedings{cheng2023beagle,
  title = {BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense},
  author = {Cheng, Siyuan and Tao, Guanhong and Liu, Yingqi and An, Shengwei and Xu, Xiangzhe and Feng, Shiwei and Shen, Guangyu and Zhang, Kaiyuan and Xu, Qiuling and Ma, Shiqing and others},
  booktitle = {Proceedings of the 30th Network and Distributed System Security Symposium},
  year = {2023},
  location = {San Diego, CA, USA},
}

@inproceedings{zhang2022flip,
  title = {FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning},
  author = {Zhang, Kaiyuan and Tao, Guanhong and Xu, Qiuling and Cheng, Siyuan and An, Shengwei and Liu, Yingqi and Feng, Shiwei and Shen, Guangyu and Chen, Pin-Yu and Ma, Shiqing and others},
  booktitle = {Proceedings of the Eleventh International Conference on Learning Representations},
  year = {2023},
  location = {Kigali, Rwanda},
}

@inproceedings{chen2023bird,
  title = {BIRD: Generalizable Backdoor Detection and Removal for Deep Reinforcement Learning},
  author = {Chen, Xuan and Guo, Wenbo and Tao, Guanhong and Zhang, Xiangyu and Song, Dawn},
  booktitle = {Proceedings of Thirty-seventh Conference on Neural Information Processing Systems},
  year = {2023},
  location = {New Orleans, LA, USA},
}

@inproceedings{feng2023detecting,
  title = {Detecting Backdoors in Pre-trained Encoders},
  author = {Feng, Shiwei and Tao, Guanhong and Cheng, Siyuan and Shen, Guangyu and Xu, Xiangzhe and Liu, Yingqi and Zhang, Kaiyuan and Ma, Shiqing and Zhang, Xiangyu},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages = {16352--16362},
  year = {2023},
  location = {Vancouver, Canada},
}

@inproceedings{tao2022model,
  title = {Model Orthogonalization: Class Distance Hardening in Neural Networks for Better Security},
  author = {Tao, Guanhong and Liu, Yingqi and Shen, Guangyu and Xu, Qiuling and An, Shengwei and Zhang, Zhuo and Zhang, Xiangyu},
  booktitle = {Proceedings of the 43rd IEEE Symposium on Security and Privacy},
  pages = {1372--1389},
  year = {2022},
  location = {San Francisco, CA, USA},
  organization = {IEEE Computer Society},
}

@inproceedings{liu2022piccolo,
  title = {PICCOLO: Exposing Complex Backdoors in NLP Transformer Models},
  author = {Liu, Yingqi and Shen, Guangyu and Tao, Guanhong and An, Shengwei and Ma, Shiqing and Zhang, Xiangyu},
  booktitle = {Proceedings of the 43rd IEEE Symposium on Security and Privacy},
  pages = {1561--1561},
  year = {2022},
  location = {San Francisco, CA, USA},
  organization = {IEEE Computer Society},
}

@inproceedings{tao2022ruler,
  title = {RULER: Discriminative and Iterative Adversarial Training for Deep Neural Network Fairness},
  author = {Tao*, Guanhong and Sun*, Weisong and Han*, Tingxu and Fang, Chunrong and Zhang, Xiangyu},
  booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  volume = {2022},
  year = {2022},
  location = {Singapore},
}

@inproceedings{tao2022better,
  title = {Better Trigger Inversion Optimization in Backdoor Scanning},
  author = {Tao, Guanhong and Shen, Guangyu and Liu, Yingqi and An, Shengwei and Xu, Qiuling and Ma, Shiqing and Li, Pan and Zhang, Xiangyu},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages = {13368--13378},
  year = {2022},
  location = {New Orleans, LA, USA},
}

@inproceedings{tao2020trader,
  author = {Tao, Guanhong and Ma, Shiqing and Liu, Yingqi and Xu, Qiuling and Zhang, Xiangyu},
  title = {TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural Networks},
  year = {2020},
  isbn = {9781450371216},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3377811.3380423},
  doi = {10.1145/3377811.3380423},
  booktitle = {Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering},
  pages = {986--998},
  numpages = {13},
  location = {Seoul, South Korea},
  series = {ICSE '20},
}

@inproceedings{liu2019abs,
  author = {Liu, Yingqi and Lee, Wen-Chuan and Tao, Guanhong and Ma, Shiqing and Aafer, Yousra and Zhang, Xiangyu},
  title = {ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation},
  booktitle = {Proceedings of the 26th ACM Conference on Computer and Communications Security},
  series = {CCS '19},
  year = {2019},
  isbn = {978-1-4503-6747-9},
  location = {London, United Kingdom},
  pages = {1265--1282},
  numpages = {18},
  url = {http://doi.acm.org/10.1145/3319535.3363216},
  doi = {10.1145/3319535.3363216},
  acmid = {3363216},
  publisher = {ACM},
  address = {New York, NY, USA},
  keywords = {ai trojan attacks, artificial brain stimulation, deep learning system},
  google_scholar_id = {Tyk-4Ss8FVUC},
}

@inproceedings{ma2019nic,
  author = {Ma, Shiqing and Liu, Yingqi and Tao, Guanhong and Lee, Wen{-}Chuan and Zhang, Xiangyu},
  title = {{NIC:} Detecting Adversarial Samples with Neural Network Invariant Checking},
  booktitle = {Proceedings of the 26th Network and Distributed System Security Symposium},
  year = {2019},
  location = {San Diego, CA, USA},
  url = {https://www.ndss-symposium.org/ndss-paper/nic-detecting-adversarial-samples-with-neural-network-invariant-checking/},
  google_scholar_id = {qjMakFHDy7sC},
}

@inproceedings{tao2018attacks,
  title = {Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples},
  author = {Tao, Guanhong and Ma, Shiqing and Liu, Yingqi and Zhang, Xiangyu},
  booktitle = {Proceedings of Thirty-second Conference on Neural Information Processing Systems},
  pages = {7728--7739},
  year = {2018},
  location = {Montréal, Canada},
  publisher = {Curran Associates, Inc.},
  url = {http://papers.nips.cc/paper/7998-attacks-meet-interpretability-attribute-steered-detection-of-adversarial-samples.pdf},
  google_scholar_id = {9yKSN-GCB0IC},
}