| Adversarial attacks and defenses in images, graphs and text: A review H Xu, Y Ma, H Liu, D Deb, H Liu, J Tang, A Jain, K International Journal of Automation and Computing (2020), 2020 | 812 | 2020 |
| Adversarial attacks and defenses on graphs W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang KDD Explorations 22, 19-34, 2021 | 314* | 2021 |
| To be robust or to be fair: Towards fairness in adversarial training H Xu, X Liu, Y Li, A Jain, J Tang International Conference on Machine Learning (2021), 2021 | 211 | 2021 |
| Deeprobust: a platform for adversarial attacks and defenses Y Li, W Jin, H Xu, J Tang AAAI (2021), 2021 | 203* | 2021 |
| Adversarial attacks and defenses on graphs: A review and empirical study W Jin, Y Li, H Xu, Y Wang, J Tang arXiv preprint, 2020 | 126 | 2020 |
| Graph neural networks with adaptive residual X Liu, J Ding, W Jin, H Xu, Y Ma, Z Liu, J Tang NeurIPS (2021), 2021 | 81 | 2021 |
| Diffusionshield: A watermark for copyright protection against generative diffusion models Y Cui, J Ren, H Xu, P He, H Liu, L Sun, J Tang arXiv preprint, 2023 | 60 | 2023 |
| A comprehensive survey on trustworthy recommender systems W Fan, X Zhao, X Chen, J Su, J Gao, L Wang, Q Liu, Y Wang, H Xu, ... arXiv preprint, 2022 | 51 | 2022 |
| The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) S Zeng, J Zhang, P He, Y Xing, Y Liu, H Xu, J Ren, S Wang, D Yin, ... arXiv preprint, 2024 | 48 | 2024 |
| Transferable unlearnable examples J Ren, H Xu, Y Wan, X Ma, L Sun, J Tang International Conference on Learning Representations (2023), 2022 | 42 | 2022 |
| Copyright Protection in Generative AI: A Technical Perspective J Ren, H Xu, P He, Y Cui, S Zeng, J Zhang, H Wen, J Ding, H Liu, ... arXiv preprint, 2024 | 35 | 2024 |
| A robust semantics-based watermark for large language model against paraphrasing J Ren, H Xu, Y Liu, Y Cui, S Wang, D Yin, J Tang NACCL Findings (2024), 2023 | 35 | 2023 |
| Jointly attacking graph neural network and its explanations W Fan, H Xu, W Jin, X Liu, X Tang, S Wang, Q Li, J Tang, J Wang, ... International Conference on Data Engineering (2023), 2023 | 32 | 2023 |
| Deep adversarial canonical correlation analysis W Fan, Y Ma, H Xu, X Liu, J Wang, Q Li, J Tang SIAM international conference on data mining (2020), 2020 | 26 | 2020 |
| Imbalanced adversarial training with reweighting W Wang, H Xu, X Liu, Y Li, B Thuraisingham, J Tang International Conference on Data Mining (2022), 2022 | 25 | 2022 |
| Adversarial attacks and defenses: Frontiers, advances and practice H Xu, Y Li, W Jin, J Tang KDD Tutorial (2020), 2020 | 23 | 2020 |
| Exploring memorization in fine-tuned language models S Zeng, Y Li, J Ren, Y Liu, H Xu, P He, Y Xing, S Wang, J Tang, D Yin arXiv preprint, 2023 | 20 | 2023 |
| Covariance-insured screening K He, J Kang, HG Hong, J Zhu, Y Li, H Lin, H Xu, Y Li Computational statistics & data analysis (2019), 2019 | 17 | 2019 |
| Unveiling and mitigating memorization in text-to-image diffusion models through cross attention J Ren, Y Li, S Zeng, H Xu, L Lyu, Y Xing, J Tang European Conference on Computer Vision, 340-356, 2024 | 16 | 2024 |
| FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models Y Cui, J Ren, Y Lin, H Xu, P He, Y Xing, W Fan, H Liu, J Tang arXiv preprint, 2023 | 15 | 2023 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State Universityایمیل تأیید شده در msu.edu
