Hanwei ZHANG

Hanwei ZHANG

Publications

Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights

Published in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

This paper is about systematized evaluation of transferable adversarial robustness on image classification. Read more

Recommended citation: Zhao, Z., Zhang, H., Li, R., Sicre, R., Amsaleg, L., Backes, M., ... & Shen, C. (2025). Revisiting Transferable Adversarial Images: Systemization, Evaluation, and New Insights. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://ieeexplore.ieee.org/abstract/document/11164808/

Saliency Maps Give a False Sense of Explanability to Image Classifiers: An Empirical Evaluation across Methods and Metrics

Published in The 16th Asian Conference on Machine Learning (Conference Track), 2024

This paper is about an empirical evaluation across saliency methods and corresponding explainable metrics. Read more

Recommended citation: Zhang, H., Figueroa, F. T., & Hermanns, H. (2024). Saliency Maps Give a False Sense of Explanability to Image Classifiers: An Empirical Evaluation across Methods and Metrics. In The 16th Asian Conference on Machine Learning (Conference Track). https://raw.githubusercontent.com/mlresearch/v260/main/assets/zhang25a/zhang25a.pdf

Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds

Published in International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (SETTA 2024), 2024

This paper is about adversarial robustness on 3D point clouds. Read more

Recommended citation: Zhang, H., Cheng, L., He, Q., Huang, W., Li, R., Sicre, R., ... & Zhang, L. (2024, November). Eidos: Efficient, imperceptible adversarial 3d point clouds. In International Symposium on Dependable Software Engineering: Theories, Tools, and Applications (pp. 310-326). Singapore: Springer Nature Singapore. https://link.springer.com/chapter/10.1007/978-981-96-0602-3_17

Traceability and accountability by construction

Published in International Symposium on Leveraging Applications of Formal Methods, 2024

This paper is about how to build an accountable and traceable AI system through cryptographic signatures. Read more

Recommended citation: Wenzel, J., Köhl, M. A., Sterz, S., Zhang, H., Schmidt, A., Fetzer, C., & Hermanns, H. (2024, October). Traceability and accountability by construction. In International Symposium on Leveraging Applications of Formal Methods (pp. 258-280). Cham: Springer Nature Switzerland. https://link.springer.com/chapter/10.1007/978-3-031-75387-9_16

NeRFail: Neural Radiance Fields-Based Multiview Adversarial Attack

Published in Proceeding of the 38th AAAI Conference on Artificial Intelligence, 2024

This paper is about adversarial robustness on NeRF. Read more

Recommended citation: Jiang, W., Zhang, H., Wang, X., Guo, Z., & Wang, H. (2024, March). Nerfail: Neural radiance fields-based multiview adversarial attack. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 19, pp. 21197-21205). https://ojs.aaai.org/index.php/AAAI/article/view/30113