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Chawin Sitawarin
Chawin Sitawarin
Postdoctoral Researcher @ Meta
Verifierad e-postadress på meta.com - Startsida
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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
410*2018
Darts: Deceiving autonomous cars with toxic signs
C Sitawarin, AN Bhagoji, A Mosenia, M Chiang, P Mittal
arXiv preprint arXiv:1802.06430, 2018
342*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
802019
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
732017
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
66*2021
On the robustness of deep k-nearest neighbors
C Sitawarin, D Wagner
2019 IEEE Security and Privacy Workshops (SPW), 1-7, 2019
63*2019
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
62*2018
Defending against adversarial examples with k-nearest neighbor
C Sitawarin, D Wagner
arXiv preprint arXiv:1906.09525, 2019
292019
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
192019
Minimum-norm adversarial examples on KNN and KNN based models
C Sitawarin, D Wagner
2020 IEEE Security and Privacy Workshops (SPW), 34-40, 2020
182020
Demystifying the adversarial robustness of random transformation defenses
C Sitawarin, ZJ Golan-Strieb, D Wagner
International Conference on Machine Learning, 20232-20252, 2022
162022
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
102024
Part-Based Models Improve Adversarial Robustness
C Sitawarin, K Pongmala, Y Chen, N Carlini, D Wagner
The Eleventh International Conference on Learning Representations, 2023
92023
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
82018
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
72023
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
72023
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
62021
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
52023
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
52021
StruQ: Defending Against Prompt Injection with Structured Queries
S Chen, J Piet, C Sitawarin, D Wagner
arXiv preprint arXiv:2402.06363, 2024
32024
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Artiklar 1–20