APPCLASSIFIER: Automated App Inference on Encrypted Traffic via Meta Data Analysis. - Chong Xiang 001; Qingrong Chen; Minhui Xue 001; Haojin Zhu (GLOBECOM, 2018)

Differentially Private Data Generative Models. - Qingrong Chen; Chong Xiang 001; Minhui Xue 001; Bo Li 026; Nikita Borisov; Dali Kaafar; Haojin Zhu (CoRR, 2018)

Generating 3D Adversarial Point Clouds. - Chong Xiang 001; Charles R. Qi; Bo Li 026 (CVPR, 2019)

No-jump-into-latency in China's internet!: toward last-mile hop count based IP geo-localization. - Chong Xiang 001; Xinyu Wang 004; Qingrong Chen; Minhui Xue 001; Zhaoyu Gao; Haojin Zhu; Cailian Chen; Qiuhua Fan (IWQoS, 2019)

Voiceprint Mimicry Attack Towards Speaker Verification System in Smart Home. - Lei Zhang; Yan Meng 001; Jiahao Yu 001; Chong Xiang 001; Brandon Falk; Haojin Zhu (INFOCOM, 2020)

PatchGuard: Provable Defense against Adversarial Patches Using Masks on Small Receptive Fields. - Chong Xiang 001; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal (CoRR, 2020)

DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attacks. - Chong Xiang 001; Prateek Mittal (CCS, 2021)

PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking. - Chong Xiang 001; Arjun Nitin Bhagoji; Vikash Sehwag; Prateek Mittal (USENIX Security Symposium, 2021)

Improving Adversarial Robustness Using Proxy Distributions. - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (CoRR, 2021)

PatchGuard++: Efficient Provable Attack Detection against Adversarial Patches. - Chong Xiang 001; Prateek Mittal (CoRR, 2021)

Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? - Vikash Sehwag; Saeed Mahloujifar; Tinashe Handina; Sihui Dai; Chong Xiang 001; Mung Chiang; Prateek Mittal (ICLR, 2022)

PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier. - Chong Xiang 001; Saeed Mahloujifar; Prateek Mittal (USENIX Security Symposium, 2022)

MultiRobustBench: Benchmarking Robustness Against Multiple Attacks. - Sihui Dai; Saeed Mahloujifar; Chong Xiang 001; Vikash Sehwag; Pin-Yu Chen; Prateek Mittal (ICML, 2023)

ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking. - Chong Xiang 001; Alexander Valtchanov; Saeed Mahloujifar; Prateek Mittal (SP, 2023)

Short: Certifiably Robust Perception Against Adversarial Patch Attacks: A Survey. - Chong Xiang 001; Chawin Sitawarin; Tong Wu; Prateek Mittal (VehicleSec, 2023)

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses. - Chong Xiang 001; Tong Wu; Sihui Dai; Jonathan Petit; Suman Jana; Prateek Mittal (USENIX Security Symposium, 2024)

Position Paper: Beyond Robustness Against Single Attack Types. - Sihui Dai; Chong Xiang 001; Tong Wu; Prateek Mittal (CoRR, 2024)

Certifiably Robust RAG against Retrieval Corruption. - Chong Xiang 001; Tong Wu; Zexuan Zhong; David A. Wagner 001; Danqi Chen 001; Prateek Mittal (CoRR, 2024)

PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches. - Dennis Jacob; Chong Xiang 001; Prateek Mittal (CVPR, 2025)

Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy. - Tong Wu; Shujian Zhang; Kaiqiang Song; Silei Xu; Sanqiang Zhao; Ravi Agrawal; Sathish Reddy Indurthi; Chong Xiang 001; Prateek Mittal; Wenxuan Zhou (ICLR, 2025)

Effectively Controlling Reasoning Models through Thinking Intervention. - Tong Wu; Chong Xiang 001; Jiachen T. Wang; Prateek Mittal (CoRR, 2025)

Does More Inference-Time Compute Really Help Robustness? - Tong Wu; Chong Xiang 001; Jiachen T. Wang; Weichen Yu; Chawin Sitawarin; Vikash Sehwag; Prateek Mittal (CoRR, 2025)