| DANAA: Towards transferable attacks with double adversarial neuron attribution Z Jin, Z Zhu, X Wang, J Zhang, J Shen, H Chen International Conference on Advanced Data Mining and Applications, 456-470, 2023 | 10 | 2023 |
| GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model Z Zhu, H Chen, X Wang, J Zhang, Z Jin, KKR Choo, J Shen, D Yuan Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | 9 | 2024 |
| Improving adversarial transferability via frequency-based stationary point search Z Zhu, H Chen, J Zhang, X Wang, Z Jin, Q Lu, J Shen, KKR Choo Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 8 | 2023 |
| MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks Z Zhu, H Chen, J Zhang, X Wang, Z Jin, M Xue, D Zhu, KKR Choo Proceedings of the AAAI Conference on Artificial Intelligence 38 (15), 17228 …, 2024 | 6 | 2024 |
| AttEXplore: Attribution for Explanation with model parameters eXploration Z Zhu, H Chen, J Zhang, X Wang, Z Jin, J Xue, FD Salim The Twelfth International Conference on Learning Representations, 0 | 4 | |
| Iterative Search Attribution for Deep Neural Networks Z Zhu, H Chen, X Wang, J Zhang, Z Jin, J Xue, J Shen Forty-first International Conference on Machine Learning, 0 | 4 | |
| Benchmarking Transferable Adversarial Attacks. Z Jin, J Zhang, Z Zhu, H Chen CoRR, 2024 | 3 | 2024 |
| Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution Z Zhu, Z Jin, X Wang, J Zhang, H Chen, KKR Choo IEEE Transactions on Artificial Intelligence, 2024 | 2 | 2024 |
| FVW: Finding Valuable Weight on Deep Neural Network for Model Pruning Z Zhu, H Chen, Z Jin, X Wang, J Zhang, M Xue, Q Lu, J Shen, KKR Choo Proceedings of the 32nd ACM International Conference on Information and …, 2023 | 2 | 2023 |
| Enhancing Model Interpretability with Local Attribution over Global Exploration Z Zhu, Z Jin, J Zhang, H Chen Proceedings of the 32nd ACM International Conference on Multimedia, 5347-5355, 2024 | 1 | 2024 |
| Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks Z Jin, J Zhang, Z Zhu, X Wang, Y Huang, H Chen arXiv preprint arXiv:2408.12670, 2024 | 1 | 2024 |
| Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack Z Jin, J Zhang, Z Zhu, C Zhang, J Huang, J Zhou, F Chen arXiv preprint arXiv:2408.07733, 2024 | 1 | 2024 |
| DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks Z Zhu, J Zhang, X Wang, Z Jin, H Chen arXiv preprint arXiv:2406.07580, 2024 | 1 | 2024 |
| POSTER: ML-Compass: A Comprehensive Assessment Framework for Machine Learning Models Z Jin, Z Zhu, H Hu, M Xue, H Chen Proceedings of the 2023 ACM Asia Conference on Computer and Communications …, 2023 | 1 | 2023 |
| Enhancing Transferable Adversarial Attacks on Vision Transformers through Gradient Normalization Scaling and High-Frequency Adaptation Z Zhu, X Wang, Z Jin, J Zhang, H Chen The Twelfth International Conference on Learning Representations, 0 | 1 | |
| AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems Z Zhu, Z Jin, H Hu, M Xue, R Sun, S Camtepe, P Gauravaram, H Chen arXiv preprint arXiv:2411.06146, 2024 | | 2024 |
| Improving Adversarial Transferability via Frequency-Guided Sample Relevance Attack X Wang, Z Jin, Z Zhu, J Zhang, H Chen Proceedings of the 33rd ACM International Conference on Information and …, 2024 | | 2024 |
| Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing Z Jin, J Zhang, Z Zhu, C Zhang, J Huang, J Zhou, F Chen arXiv preprint arXiv:2408.12673, 2024 | | 2024 |
| Short: Benchmarking transferable adversarial attacks Z Jin, J Zhang, Z Zhu, H Chen arXiv preprint arXiv:2402.00418, 2024 | | 2024 |
| Towards Minimising Perturbation Rate for Adversarial Machine Learning with Pruning Z Zhu, J Zhang, Z Jin, X Wang, M Xue, J Shen, KKR Choo, H Chen Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | | 2023 |
