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 | 2952 | 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 | 957 | 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 | 841 | 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 | 457 | 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 | 371 | 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 | 290 | 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 | 277 | 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 | 276 | 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 | 251* | 2018 |
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 | 184 | 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 | 156 | 2020 |
Mixup inference: Better exploiting mixup to defend adversarial attacks T Pang, K Xu, J Zhu International Conference on Learning Representations (ICLR 2020), 2020 | 130 | 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 | 128 | 2023 |
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 | 115 | 2020 |
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 | 106 | 2022 |
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 | 102 | 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 | 83 | 2021 |
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 | 74 | 2023 |
Lorahub: Efficient cross-task generalization via dynamic lora composition C Huang, Q Liu, BY Lin, T Pang, C Du, M Lin arXiv preprint arXiv:2307.13269, 2023 | 68 | 2023 |
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 | 68 | 2022 |