| Boosting adversarial attacks with momentum Y Dong, F Liao, T Pang, H Su, J Zhu, X Hu, J Li IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018 | 3446 | 2018 |
| Defense against adversarial attacks using high-level representation guided denoiser F Liao, M Liang, Y Dong, T Pang, X Hu, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), 2018 | 1083 | 2018 |
| Evading defenses to transferable adversarial examples by translation-invariant attacks Y Dong, T Pang, H Su, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), 2019 | 1016 | 2019 |
| Improving adversarial robustness via promoting ensemble diversity T Pang, K Xu, C Du, N Chen, J Zhu International Conference on Machine Learning (ICML 2019), 2019 | 526 | 2019 |
| Adversarial attacks and defences competition A Kurakin, I Goodfellow, S Bengio, Y Dong, F Liao, M Liang, T Pang, ... The NIPS'17 Competition: Building Intelligent Systems, 195-231, 2018 | 392 | 2018 |
| Benchmarking adversarial robustness on image classification Y Dong, QA Fu, X Yang, T Pang, H Su, Z Xiao, J Zhu IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), 2020 | 340 | 2020 |
| Bag of tricks for adversarial training T Pang, X Yang, Y Dong, H Su, J Zhu International Conference on Learning Representations (ICLR 2021), 2021 | 305 | 2021 |
| Improving black-box adversarial attacks with a transfer-based prior S Cheng, Y Dong, T Pang, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 2019 | 304 | 2019 |
| Towards robust detection of adversarial examples T Pang, C Du, Y Dong, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 2018 | 266* | 2018 |
| Better diffusion models further improve adversarial training Z Wang*, T Pang*, C Du, M Lin, W Liu, S Yan International Conference on Machine Learning (ICML 2023), 2023 | 251 | 2023 |
| Rethinking softmax cross-entropy loss for adversarial robustness T Pang, K Xu, Y Dong, C Du, N Chen, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 209 | 2020 |
| On evaluating adversarial robustness of large vision-language models Y Zhao*, T Pang*, C Du, X Yang, C Li, NM Cheung, M Lin Annual Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 191 | 2023 |
| Boosting adversarial training with hypersphere embedding T Pang, X Yang, Y Dong, K Xu, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 167 | 2020 |
| Lorahub: Efficient cross-task generalization via dynamic lora composition C Huang, Q Liu, BY Lin, T Pang, C Du, M Lin Conference on Language Modeling (COLM 2024), 2024 | 162 | 2024 |
| Robustness and Accuracy Could Be Reconcilable by (Proper) Definition T Pang, M Lin, X Yang, J Zhu, S Yan International Conference on Machine Learning (ICML 2022), 2022 | 147 | 2022 |
| Adversarial Distributional Training for Robust Deep Learning Z Deng, Y Dong, T Pang, H Su, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 140 | 2020 |
| Mixup inference: Better exploiting mixup to defend adversarial attacks T Pang, K Xu, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 137 | 2020 |
| Black-box Detection of Backdoor Attacks with Limited Information and Data Y Dong, X Yang, Z Deng, T Pang, Z Xiao, H Su, J Zhu International Conference on Computer Vision (ICCV 2021), 2021 | 136 | 2021 |
| A recipe for watermarking diffusion models Y Zhao, T Pang, C Du, X Yang, NM Cheung, M Lin arXiv preprint arXiv:2303.10137, 2023 | 132 | 2023 |
| Towards face encryption by generating adversarial identity masks X Yang, Y Dong, T Pang, J Zhu, H Su International Conference on Computer Vision (ICCV 2021), 2021 | 109 | 2021 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityPatvirtintas el. paštas mail.tsinghua.edu.cn