Chawin Sitawarin

According to our database, Chawin Sitawarin authored at least 26 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Vulnerability Detection with Code Language Models: How Far Are We?

Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David A. Wagner, Baishakhi Ray, Yizheng Chen

CoRR, 2024

PAL: Proxy-Guided Black-Box Attack on Large Language Models.

Chawin Sitawarin, Norman Mu, David A. Wagner, Alexandre Araujo

CoRR, 2024

StruQ: Defending Against Prompt Injection with Structured Queries.

Sizhe Chen, Julien Piet, Chawin Sitawarin, David A. Wagner

CoRR, 2024

OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift.

Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling

Proceedings of the Forty-first International Conference on Machine Learning, 2024

PubDef: Defending Against Transfer Attacks From Public Models.

Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David A. Wagner

Proceedings of the Twelfth International Conference on Learning Representations, 2024

SPDER: Semiperiodic Damping-Enabled Object Representation.

Kathan Shah, Chawin Sitawarin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Jatmo: Prompt Injection Defense by Task-Specific Finetuning.

Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun, Basel Alomair, David A. Wagner

Proceedings of the Computer Security - ESORICS 2024, 2024

2023

Mark My Words: Analyzing and Evaluating Language Model Watermarks.

Julien Piet, Chawin Sitawarin, Vivian Fang, Norman Mu, David A. Wagner

CoRR, 2023

Defending Against Transfer Attacks From Public Models.

Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David A. Wagner

CoRR, 2023

OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift.

Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling

CoRR, 2023

Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems.

Chawin Sitawarin, Florian Tramèr, Nicholas Carlini

Proceedings of the International Conference on Machine Learning, 2023

Part-Based Models Improve Adversarial Robustness.

Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner

Proceedings of the Eleventh International Conference on Learning Representations, 2023

REAP: A Large-Scale Realistic Adversarial Patch Benchmark.

Nabeel Hingun, Chawin Sitawarin, Jerry Li, David A. Wagner

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Demystifying the Adversarial Robustness of Random Transformation Defenses.

Chawin Sitawarin, Zachary J. Golan-Strieb, David A. Wagner

Proceedings of the International Conference on Machine Learning, 2022

2021

Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams.

Chawin Sitawarin, Evgenios M. Kornaropoulos, Dawn Song, David A. Wagner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing.

Chawin Sitawarin, Supriyo Chakraborty, David A. Wagner

Proceedings of the AISec@CCS 2021: Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, 2021

2020

Improving Adversarial Robustness Through Progressive Hardening.

Chawin Sitawarin, Supriyo Chakraborty, David A. Wagner

CoRR, 2020

Minimum-Norm Adversarial Examples on KNN and KNN based Models.

Chawin Sitawarin, David A. Wagner

Proceedings of the 2020 IEEE Security and Privacy Workshops, 2020

2019

Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples.

Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

CoRR, 2019

On the Robustness of Deep K-Nearest Neighbors.

Chawin Sitawarin, David A. Wagner

Proceedings of the 2019 IEEE Security and Privacy Workshops, 2019

Analyzing the Robustness of Open-World Machine Learning.

Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal

Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, 2019

2018

DARTS: Deceiving Autonomous Cars with Toxic Signs.

Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal

CoRR, 2018

Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos.

Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang

CoRR, 2018

Enhancing robustness of machine learning systems via data transformations.

Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal

Proceedings of the 52nd Annual Conference on Information Sciences and Systems, 2018

Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints.

Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal

Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018

2017

Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars.

Mark Martinez, Chawin Sitawarin, Kevin Finch, Lennart Meincke, Alex Yablonski, Alain L. Kornhauser

CoRR, 2017