| 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 | 373* | 2018 |
| Darts: Deceiving autonomous cars with toxic signs C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal arXiv preprint arXiv:1802.06430, 2018 | 324* | 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 | 65 | 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 | 59 | 2017 |
| 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 | 57 | 2018 |
| On the robustness of deep k-nearest neighbors C Sitawarin, D Wagner 2019 IEEE Security and Privacy Workshops (SPW), 1-7, 2019 | 55* | 2019 |
| 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 | 49* | 2021 |
| Defending against adversarial examples with k-nearest neighbor C Sitawarin, D Wagner arXiv preprint arXiv:1906.09525, 2019 | 29 | 2019 |
| 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 | 18 | 2019 |
| Minimum-norm adversarial examples on KNN and KNN based models C Sitawarin, D Wagner 2020 IEEE Security and Privacy Workshops (SPW), 34-40, 2020 | 17* | 2020 |
| Demystifying the adversarial robustness of random transformation defenses C Sitawarin, ZJ Golan-Strieb, D Wagner International Conference on Machine Learning, 20232-20252, 2022 | 10 | 2022 |
| Photonics Res., 6 (5) C Sitawarin, W Jin, Z Lin, A Rodriguez B82-B89, 2018 | 7* | 2018 |
| Part-Based Models Improve Adversarial Robustness C Sitawarin, K Pongmala, Y Chen, N Carlini, D Wagner The Eleventh International Conference on Learning Representations, 2023 | 6 | 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 | 6 | 2018 |
| Mitigating Adversarial Training Instability with Batch Normalization AP Sridhar, C Sitawarin, D Wagner Security and Safety in Machine Learning Systems Workshop at ICLR 2021, 2021 | 5 | 2021 |
| Adversarial examples for k-nearest neighbor classifiers based on higher-order voronoi diagrams C Sitawarin, E Kornaropoulos, D Song, D Wagner Advances in Neural Information Processing Systems 34, 15486-15497, 2021 | 4 | 2021 |
| Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems C Sitawarin, F Tramèr, N Carlini Proceedings of the 40th International Conference on Machine Learning 202 …, 2023 | 2 | 2023 |
| Improving the Accuracy-Robustness Trade-Off for Dual-Domain Adversarial Training C Sitawarin, A Sridhar, D Wagner ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning, 2021 | 2 | 2021 |
| 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 | | 2023 |
| SPDER: Semiperiodic Damping-Enabled Object Representation K Shah, C Sitawarin arXiv preprint arXiv:2306.15242, 2023 | | 2023 |
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu
