| Boosting the transferability of adversarial samples via attention W Wu, Y Su, X Chen, S Zhao, I King, MR Lyu, YW Tai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 62 | 2020 |
| Improving the transferability of adversarial samples with adversarial transformations W Wu, Y Su, MR Lyu, I King Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 38 | 2021 |
| Towards global explanations of convolutional neural networks with concept attribution W Wu, Y Su, X Chen, S Zhao, I King, MR Lyu, YW Tai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 34 | 2020 |
| Deep validation: Toward detecting real-world corner cases for deep neural networks W Wu, H Xu, S Zhong, MR Lyu, I King 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems …, 2019 | 32 | 2019 |
| Improving adversarial transferability via neuron attribution-based attacks J Zhang, W Wu, J Huang, Y Huang, W Wang, Y Su, MR Lyu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 19 | 2022 |
| NV-DNN: towards fault-tolerant DNN systems with N-version programming H Xu, Z Chen, W Wu, Z Jin, S Kuo, M Lyu 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems …, 2019 | 18 | 2019 |
| A personalized limb rehabilitation training system for stroke patients W Wu, D Wang, T Wang, M Liu 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO), 1924 …, 2016 | 10 | 2016 |
| MTTM: Metamorphic Testing for Textual Content Moderation Software W Wang, J Huang, W Wu, J Zhang, Y Huang, S Li, P He, M Lyu arXiv preprint arXiv:2302.05706, 2023 | 1 | 2023 |
| Improving the Transferability of Adversarial Samples by Path-Augmented Method J Zhang, J Huang, W Wang, Y Li, W Wu, X Wang, Y Su, MR Lyu arXiv preprint arXiv:2303.15735, 2023 | | 2023 |
| Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization J Zhang, Y Huang, W Wu, MR Lyu arXiv preprint arXiv:2303.15754, 2023 | | 2023 |
| On the Robustness and Interpretability of Deep Learning Models W Wu PQDT-Global, 2021 | | 2021 |
| Supplementary Materials: Towards Global Explanations of Convolutional Neural Networks with Concept Attribution W Wu, Y Su, X Chen, S Zhao, I King, MR Lyu, YW Tai | | |
