  | HU, H. (2023). Privacy Attacks and Protection in Generative Models [Doctoral thesis, Unilu - Université du Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/58928 |
 | Waghorne, J., Howard, C., Hu, H., Pang, J., Peveler, W., Harris, L., & Barrera, O. (May 2023). The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues. Frontiers in Materials, 10. doi:10.3389/fmats.2023.1092647  Peer Reviewed verified by ORBi |
 | HU, H., & PANG, J. (2023). Loss and Likelihood Based Membership Inference of Diffusion Models. In Proceedings of the 26th Information Security Conference (ISC'23) (pp. 121-141). Springer Nature Switzerland. doi:10.1007/978-3-031-49187-0_7 Peer reviewed |
 | Hu, H., & Pang, J. (2021). Membership Inference Attacks against GANs by Leveraging Over-representation Regions. In Proceedings of the 27th ACM SIGSAC Conference on Computer and Communications Security (CCS'21) (pp. 2387-2389). ACM. doi:10.1145/3460120.3485338 Peer reviewed |
 | Hu, H., & Pang, J. (2021). Stealing Machine Learning Models: Attacks and Countermeasures for Generative Adversarial Networks. In Proceedings of the 37th Annual Computer Security Applications Conference (ACSAC'21) (pp. 1-16). ACM. doi:10.1145/3485832.3485838 Peer reviewed |
