publications by categories in reversed chronological order.


  1. ICML
    Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
    Chawin Sitawarin, Florian Tramèr, and Nicholas Carlini
    In Proceedings of the 40th International Conference on Machine Learning, Aug 2023
  2. ICLR
    Part-Based Models Improve Adversarial Robustness
    Chawin Sitawarin, Kornrapat Pongmala, Yizheng Chen, Nicholas Carlini, and David Wagner
    In International Conference on Learning Representations, May 2023
  3. VehicleSec
    Short: Certifiably Robust Perception against Adversarial Patch Attacks: A Survey
    Chong Xiang, Chawin Sitawarin, Tong Wu, and Prateek Mittal
    In 1st Symposium on Vehicle Security and Privacy (VehicleSec), Mar 2023
    Co-located with NDSS 2023. Best Short/WIP Paper Award Runner-Up.


  1. REAP: A Large-Scale Realistic Adversarial Patch Benchmark
    Nabeel Hingun, Chawin Sitawarin, Jerry Li, and David Wagner
    Under submission, Oct 2022
  2. ICML
    Demystifying the Adversarial Robustness of Random Transformation Defenses
    Chawin Sitawarin, Zachary Golan-Strieb, and David Wagner
    In Proceedings of the 39th International Conference on Machine Learning, Oct 2022
    Best Paper Award from AAAI-2022 Workshop on Adversarial Machine Learning and Beyond


  1. NeurIPS
    Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams
    Chawin Sitawarin, Evgenios M Kornaropoulos, Dawn Song, and David Wagner
    In Advances in Neural Information Processing Systems, Oct 2021
  2. Improving the Accuracy-Robustness Trade-off for Dual-Domain Adversarial Training
    Chawin Sitawarin, Arvind P Sridhar, and David Wagner
    In Workshop on Uncertainty and Robustness in Deep Learning, Jul 2021
  3. AISec
    SAT: Improving Adversarial Training via Curriculum-Based Loss Smoothing
    Chawin Sitawarin, Supriyo Chakraborty, and David Wagner
    In Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security, Jul 2021
  4. Mitigating Adversarial Training Instability with Batch Normalization
    Arvind P Sridhar, Chawin Sitawarin, and David Wagner
    In Security and Safety in Machine Learning Systems Workshop, May 2021


  1. DLS
    Minimum-Norm Adversarial Examples on KNN and KNN Based Models
    Chawin Sitawarin, and David Wagner
    In 2020 IEEE Security and Privacy Workshops (SPW), May 2020


  1. AISec
    Analyzing the Robustness of Open-World Machine Learning
    Vikash Sehwag, Arjun Nitin Bhagoji, Liwei Song, Chawin Sitawarin, Daniel Cullina, Mung Chiang, and Prateek Mittal
    In Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security, May 2019
  2. Defending against Adversarial Examples with K-Nearest Neighbor
    Chawin Sitawarin, and David Wagner
    arXiv:1906.09525 [cs], Jun 2019
  3. DLS
    On the Robustness of Deep K-Nearest Neighbors
    Chawin Sitawarin, and David Wagner
    In 2019 IEEE Security and Privacy Workshops (SPW), May 2019


  1. CISS
    Enhancing Robustness of Machine Learning Systems via Data Transformations
    Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, and Prateek Mittal
    In 52nd Annual Conference on Information Sciences and Systems (CISS), May 2018
  2. CCS
    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, and Prateek Mittal
    In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, Oct 2018
  3. DARTS: Deceiving Autonomous Cars with Toxic Signs
    Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Mung Chiang, and Prateek Mittal
    arXiv:1802.06430 [cs], May 2018
  4. Photon. Res.
    Inverse-designed photonic fibers and metasurfaces for nonlinear frequency conversion (Invited)
    Chawin Sitawarin, Weiliang Jin, Zin Lin, and Alejandro W. Rodriguez
    Photon. Res., May 2018
  5. DLS
    Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logos
    Chawin Sitawarin, Arjun Nitin Bhagoji, Arsalan Mosenia, Prateek Mittal, and Mung Chiang
    arXiv:1801.02780 [cs], Mar 2018


  1. Beyond Grand Theft Auto v for Training, Testing and Enhancing Deep Learning in Self Driving Cars
    Mark Anthony Martinez, Chawin Sitawarin, Kevin Finch, Lennart Meincke, Alexander Yablonski, and Alain Kornhauser
    arXiv:1712.01397 [cs], Dec 2017


  1. CLEO
    Inverse-Designed Nonlinear Nanophotonic Structures: Enhanced Frequency Conversion at the Nano Scale
    Zin Lin, Chawin Sitawarin, Marko Loncar, and Alejandro W. Rodriguez
    In 2016 Conference on Lasers and Electro-Optics, CLEO 2016, Dec 2016