| Enhancing robustness of machine learning systems via data transformations AN Bhagoji, D Cullina, C Sitawarin, P Mittal 2018 52nd Annual Conference on Information Sciences and Systems (CISS), 1-5, 2018 | 443* | 2018 |
| Darts: Deceiving autonomous cars with toxic signs C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal arXiv preprint arXiv:1802.06430, 2018 | 388* | 2018 |
| Analyzing the robustness of open-world machine learning V Sehwag, AN Bhagoji, L Song, C Sitawarin, D Cullina, M Chiang, P Mittal Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019 | 98 | 2019 |
| Beyond grand theft auto V for training, testing and enhancing deep learning in self driving cars M Martinez, C Sitawarin, K Finch, L Meincke, A Yablonski, A Kornhauser arXiv preprint arXiv:1712.01397, 2017 | 81 | 2017 |
| Sat: Improving adversarial training via curriculum-based loss smoothing C Sitawarin, S Chakraborty, D Wagner Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security …, 2021 | 72* | 2021 |
| Inverse-designed photonic fibers and metasurfaces for nonlinear frequency conversion C Sitawarin, W Jin, Z Lin, AW Rodriguez Photonics Research 6 (5), B82-B89, 2018 | 66* | 2018 |
| On the robustness of deep k-nearest neighbors C Sitawarin, D Wagner 2019 IEEE Security and Privacy Workshops (SPW), 1-7, 2019 | 65* | 2019 |
| Jatmo: Prompt injection defense by task-specific finetuning J Piet, M Alrashed, C Sitawarin, S Chen, Z Wei, E Sun, B Alomair, ... Computer Security – ESORICS 2024, 2024 | 35 | 2024 |
| StruQ: Defending against prompt injection with structured queries S Chen, J Piet, C Sitawarin, D Wagner arXiv preprint arXiv:2402.06363, 2024 | 32 | 2024 |
| Defending against adversarial examples with k-nearest neighbor C Sitawarin, D Wagner arXiv preprint arXiv:1906.09525, 2019 | 31 | 2019 |
| Pal: Proxy-guided black-box attack on large language models C Sitawarin, N Mu, D Wagner, A Araujo arXiv preprint arXiv:2402.09674, 2024 | 23 | 2024 |
| Minimum-norm adversarial examples on KNN and KNN based models C Sitawarin, D Wagner 2020 IEEE Security and Privacy Workshops (SPW), 34-40, 2020 | 23 | 2020 |
| Mark my words: Analyzing and evaluating language model watermarks J Piet, C Sitawarin, V Fang, N Mu, D Wagner arXiv preprint arXiv:2312.00273, 2023 | 22 | 2023 |
| Demystifying the adversarial robustness of random transformation defenses C Sitawarin, ZJ Golan-Strieb, D Wagner International Conference on Machine Learning, 20232-20252, 2022 | 21 | 2022 |
| Better the devil you know: An analysis of evasion attacks using out-of-distribution adversarial examples V Sehwag, AN Bhagoji, L Song, C Sitawarin, D Cullina, M Chiang, P Mittal arXiv preprint arXiv:1905.01726, 2019 | 21 | 2019 |
| Vulnerability detection with code language models: How far are we? Y Ding, Y Fu, O Ibrahim, C Sitawarin, X Chen, B Alomair, D Wagner, ... arXiv preprint arXiv:2403.18624, 2024 | 18 | 2024 |
| REAP: A Large-Scale Realistic Adversarial Patch Benchmark N Hingun, C Sitawarin, J Li, D Wagner Proceedings of the IEEE/CVF international conference on computer vision (ICCV), 2023 | 12 | 2023 |
| Part-Based Models Improve Adversarial Robustness C Sitawarin, K Pongmala, Y Chen, N Carlini, D Wagner The Eleventh International Conference on Learning Representations, 2023 | 11 | 2023 |
| Not all pixels are born equal: An analysis of evasion attacks under locality constraints V Sehwag, C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications …, 2018 | 9 | 2018 |
| OODRobustBench: a benchmark and large-scale analysis of adversarial robustness under distribution shift L Li, Y Wang, C Sitawarin, M Spratling Proceedings of the 41st International Conference on Machine Learning, 2024 | 8* | 2024 |
David WagnerProfessor of Computer Science, UC BerkeleyVerified email at cs.berkeley.edu
