Chaowei Xiao
Chaowei Xiao
PhD candidate, University of Michigan
Verifierad e-postadress på - Startsida
TitelCiteras avÅr
Robust physical-world attacks on deep learning visual classification
K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, C Xiao, A Prakash, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018
Tagoram: Real-time tracking of mobile RFID tags to high precision using COTS devices
L Yang, Y Chen, XY Li, C Xiao, M Li, Y Liu
Proceedings of the 20th annual international conference on Mobile computing …, 2014
Generating Adversarial Examples with Adversarial Networks
C Xiao, B Li, JY Zhu, W He, M Liu, D Song
International Joint Conferences on Artificial Intelligence Organization …, 2018
Spatially Transformed Adversarial Examples
C Xiao, JY Zhu, B Li, W He, M Liu, D Song
International Conference on Learning Representations, 2018
Static power of mobile devices: Self-updating radio maps for wireless indoor localization
C Wu, Z Yang, C Xiao, C Yang, Y Liu, M Liu
2015 IEEE Conference on Computer Communications (INFOCOM), 2497-2505, 2015
Automatic radio map adaptation for indoor localization using smartphones
C Wu, Z Yang, C Xiao
IEEE Transactions on Mobile Computing 17 (3), 517-528, 2017
Meshadv: Adversarial meshes for visual recognition
C Xiao, D Yang, B Li, J Deng, M Liu
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Hardening Classifiers against Evasion: the Good, the Bad, and the Ugly
L Tong, B Li, C Hajaj, C Xiao, Y Vorobeychik
arXiv preprint arXiv:1708.08327, 2017
Characterizing adversarial examples based on spatial consistency information for semantic segmentation
C Xiao, R Deng, B Li, F Yu, M Liu, D Song
Proceedings of the European Conference on Computer Vision (ECCV), 217-234, 2018
Patch me if you can: A study on the effects of individual user behavior on the end-host vulnerability state
A Sarabi, Z Zhu, C Xiao, M Liu, T Dumitraş
International Conference on Passive and Active Network Measurement, 113-125, 2017
Data poisoning attack against unsupervised node embedding methods
M Sun, J Tang, H Li, B Li, C Xiao, Y Chen, D Song
arXiv preprint arXiv:1810.12881, 2018
From patching delays to infection symptoms: using risk profiles for an early discovery of vulnerabilities exploited in the wild
C Xiao, A Sarabi, Y Liu, B Li, M Liu, T Dumitras
27th {USENIX} Security Symposium ({USENIX} Security 18), 903-918, 2018
Improving Robustness of {ML} Classifiers against Realizable Evasion Attacks Using Conserved Features
L Tong, B Li, C Hajaj, C Xiao, N Zhang, Y Vorobeychik
28th {USENIX} Security Symposium ({USENIX} Security 19), 285-302, 2019
Adversarial sensor attack on lidar-based perception in autonomous driving
Y Cao, C Xiao, B Cyr, Y Zhou, W Park, S Rampazzi, QA Chen, K Fu, ...
Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications …, 2019
Adversarial objects against lidar-based autonomous driving systems
Y Cao, C Xiao, D Yang, J Fang, R Yang, M Liu, B Li
arXiv preprint arXiv:1907.05418, 2019
SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
H Qiu, C Xiao, L Yang, X Yan, H Lee, B Li
arXiv preprint arXiv:1906.07927, 2019
Characterizing Attacks on Deep Reinforcement Learning
C Xiao, X Pan, W He, J Peng, M Sun, J Yi, B Li, D Song
arXiv preprint arXiv:1907.09470, 2019
Protecting Sensitive Attributes via Generative Adversarial Networks
A Rezaei, C Xiao, J Gao, B Li
arXiv preprint arXiv:1812.10193, 2018
Performing Co-Membership Attacks Against Deep Generative Models
KS Liu, C Xiao, B Li, J Gao
arXiv preprint arXiv:1805.09898, 2018
Towards Stable and Efficient Training of Verifiably Robust Neural Networks
H Zhang, H Chen, C Xiao, B Li, D Boning, CJ Hsieh
arXiv preprint arXiv:1906.06316, 2019
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Artiklar 1–20