| Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks W Xu, D Evans, Y Qi Network and Distributed System Security Symposium 2018, 2018 | 2080 | 2018 |
| Federated Learning in Medicine: Facilitating Multi-institutional Collaborations without Sharing Patient Data MJ Sheller, B Edwards, GA Reina, J Martin, S Pati, A Kotrotsou, ... Scientific Reports 10 (1), 1-12, 2020 | 1175 | 2020 |
| Automatically Evading Classifiers W Xu, Y Qi, D Evans Network and Distributed System Security Symposium 2016, 2016 | 521 | 2016 |
| DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples J Gao, B Wang, Z Lin, W Xu, Y Qi ICLR 2017 Workshop, 2017 | 113 | 2017 |
| Security Risks in Deep Learning Implementations Q Xiao, K Li, D Zhang, W Xu 2018 IEEE Security and Privacy Workshops (SPW), 123-128, 2018 | 83 | 2018 |
| Feature Squeezing Mitigates and Detects Carlini/Wagner Adversarial Examples W Xu, D Evans, Y Qi arXiv preprint arXiv:1705.10686, 2017 | 72 | 2017 |
| Robust principles: Architectural design principles for adversarially robust cnns SY Peng, W Xu, C Cornelius, M Hull, K Li, R Duggal, M Phute, J Martin, ... arXiv preprint arXiv:2308.16258, 2023 | 29 | 2023 |
| Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models X He, Z Li, W Xu, C Cornelius, Y Zhang arXiv preprint arXiv:2208.10445, 2022 | 25 | 2022 |
Yanjun QiFaculty @UVa CS / PhD @CMU / DATA Scholar@ NIH/ Principal Applied Scientist @ AWSVerified email at virginia.edu
