| On the robustness of backdoor-based watermarking in deep neural networks M Shafieinejad, N Lukas, J Wang, X Li, F Kerschbaum Proceedings of the 2021 ACM Workshop on Information Hiding and Multimedia …, 2021 | 117 | 2021 |
| Sok: How robust is image classification deep neural network watermarking? N Lukas, E Jiang, X Li, F Kerschbaum 2022 IEEE Symposium on Security and Privacy (SP), 787-804, 2022 | 79 | 2022 |
| Towards Robust Dataset Learning Y Wu, X Li, F Kerschbaum, H Huang, H Zhang arXiv preprint arXiv:2211.10752, 2022 | 9 | 2022 |
| Sok: How robust is deep neural network image classification watermarking N Lukas, E Jiang, X Li, F Kerschbaum IEEE Symposium on Security and Privacy, 52-69, 2022 | 7 | 2022 |
| Fast and private inference of deep neural networks by co-designing activation functions A Diaa, L Fenaux, T Humphries, M Dietz, F Ebrahimianghazani, ... 33rd USENIX Security Symposium (USENIX Security 24), 2191-2208, 2024 | 3 | 2024 |
| Recovery from non-decomposable distance oracles Z Hu, X Li, DP Woodruff, H Zhang, S Zhang IEEE Transactions on Information Theory 69 (10), 6443-6469, 2023 | 2 | 2023 |
| Improved Model Poisoning Attacks and Defenses in Federated Learning with Clustering X Li University of Waterloo, 2022 | 2 | 2022 |
| {PEPSI}: Practically Efficient Private Set Intersection in the Unbalanced Setting RA Mahdavi, N Lukas, F Ebrahimianghazani, T Humphries, B Kacsmar, ... 33rd USENIX Security Symposium (USENIX Security 24), 6453-6470, 2024 | | 2024 |
| Sok: How robust is image classification deep neural network watermarking?(extended version) N Lukas, E Jiang, X Li, F Kerschbaum arXiv preprint arXiv:2108.04974, 2021 | | 2021 |
