Chawin Sitawarin

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 33

Top Topics

Top Venues

Publications

Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David A. Wagner
PubDef: Defending Against Transfer Attacks From Public Models. ICLR (2024)
Sizhe Chen, Julien Piet, Chawin Sitawarin, David A. Wagner
StruQ: Defending Against Prompt Injection with Structured Queries. CoRR (2024)
Kathan Shah, Chawin Sitawarin
SPDER: Semiperiodic Damping-Enabled Object Representation. ICLR (2024)
Chawin Sitawarin, Norman Mu, David A. Wagner, Alexandre Araujo
PAL: Proxy-Guided Black-Box Attack on Large Language Models. CoRR (2024)
Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David A. Wagner, Baishakhi Ray, Yizheng Chen
Vulnerability Detection with Code Language Models: How Far Are We? CoRR (2024)
Lin Li, Yifei Wang, Chawin Sitawarin, Michael W. Spratling
OODRobustBench: benchmarking and analyzing adversarial robustness under distribution shift. CoRR (2023)
Nabeel Hingun, Chawin Sitawarin, Jerry Li, David A. Wagner
REAP: A Large-Scale Realistic Adversarial Patch Benchmark. ICCV (2023)
Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun, Basel Alomair, David A. Wagner
Jatmo: Prompt Injection Defense by Task-Specific Finetuning. CoRR (2023)
Julien Piet, Chawin Sitawarin, Vivian Fang, Norman Mu, David A. Wagner
Mark My Words: Analyzing and Evaluating Language Model Watermarks. CoRR (2023)
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. ICML (2023)
Chawin Sitawarin, Jaewon Chang, David Huang, Wesson Altoyan, David A. Wagner
Defending Against Transfer Attacks From Public Models. CoRR (2023)
Kathan Shah, Chawin Sitawarin
SPDER: Semiperiodic Damping-Enabled Object Representation. CoRR (2023)
Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner
Part-Based Models Improve Adversarial Robustness. ICLR (2023)
Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, David A. Wagner
Part-Based Models Improve Adversarial Robustness. CoRR (2022)
Chawin Sitawarin, Zachary J. Golan-Strieb, David A. Wagner
Demystifying the Adversarial Robustness of Random Transformation Defenses. CoRR (2022)
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. CoRR (2022)
Nabeel Hingun, Chawin Sitawarin, Jerry Li, David A. Wagner
REAP: A Large-Scale Realistic Adversarial Patch Benchmark. CoRR (2022)
Chawin Sitawarin, Zachary J. Golan-Strieb, David A. Wagner
Demystifying the Adversarial Robustness of Random Transformation Defenses. ICML (2022)
Chawin Sitawarin, Supriyo Chakraborty, David A. Wagner
SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing. AISec@CCS (2021)
Chawin Sitawarin, Evgenios M. Kornaropoulos, Dawn Song, David A. Wagner
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams. NeurIPS (2021)
Chawin Sitawarin, Evgenios M. Kornaropoulos, Dawn Song, David A. Wagner
Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams. CoRR (2020)
Chawin Sitawarin, David A. Wagner
Minimum-Norm Adversarial Examples on KNN and KNN-Based Models. CoRR (2020)
Chawin Sitawarin, Supriyo Chakraborty, David A. Wagner
Improving Adversarial Robustness Through Progressive Hardening. CoRR (2020)
Chawin Sitawarin, David A. Wagner
Minimum-Norm Adversarial Examples on KNN and KNN based Models. SP Workshops (2020)
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal
Analyzing the Robustness of Open-World Machine Learning. AISec@CCS (2019)
Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, Prateek Mittal
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. CoRR (2019)
Chawin Sitawarin, David A. Wagner
On the Robustness of Deep K-Nearest Neighbors. CoRR (2019)
Chawin Sitawarin, David A. Wagner
On the Robustness of Deep K-Nearest Neighbors. IEEE Symposium on Security and Privacy Workshops (2019)
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, Mung Chiang
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos. CoRR (2018)
Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
DARTS: Deceiving Autonomous Cars with Toxic Signs. CoRR (2018)
Vikash Sehwag, Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, Prateek Mittal
Not All Pixels are Born Equal: An Analysis of Evasion Attacks under Locality Constraints. CCS (2018)
Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, Prateek Mittal
Enhancing robustness of machine learning systems via data transformations. CISS (2018)
Mark Martinez, Chawin Sitawarin, Kevin Finch, Lennart Meincke, Alex Yablonski, Alain L. Kornhauser
Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars. CoRR (2017)