| 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 | 2287 | 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 | 812 | 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 | 595 | 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 | 369 | 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 | 323 | 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 | 225 | 2020 |
| 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 | 225 | 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 | 222* | 2018 |
| Bag of tricks for adversarial training T Pang, X Yang, Y Dong, H Su, J Zhu International Conference on Learning Representations (ICLR 2021), 2021 | 217 | 2021 |
| 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 | 146 | 2020 |
| 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 | 118 | 2020 |
| Mixup inference: Better exploiting mixup to defend adversarial attacks T Pang, K Xu, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 106 | 2020 |
| 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 | 91 | 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 | 61 | 2021 |
| 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 | 53 | 2021 |
| 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 | 50 | 2022 |
| Max-mahalanobis linear discriminant analysis networks T Pang, C Du, J Zhu International Conference on Machine Learning (ICML 2018), 2018 | 47 | 2018 |
| Exploring Memorization in Adversarial Training Y Dong, K Xu, X Yang, T Pang, Z Deng, H Su, J Zhu International Conference on Learning Representations (ICLR 2022), 2022 | 42 | 2022 |
| Efficient learning of generative models via finite-difference score matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu Annual Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 31 | 2020 |
| 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 | 25 | 2023 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityPatvirtintas el. paštas mail.tsinghua.edu.cn
