Shouling Ji
Shouling Ji
Professor, Zhejiang University & Georgia Institute of Technology
Verified email at - Homepage
Cited by
Cited by
Textbugger: Generating adversarial text against real-world applications
J Li, S Ji, T Du, B Li, T Wang
arXiv preprint arXiv:1812.05271, 2018
{CoVisor}: a compositional hypervisor for {software-defined} networks
X Jin, J Gossels, J Rexford, D Walker
12th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2015
Dual encoding for zero-example video retrieval
J Dong, X Li, C Xu, S Ji, Y He, G Yang, X Wang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
{MOPT}: Optimized mutation scheduling for fuzzers
C Lyu, S Ji, C Zhang, Y Li, WH Lee, Y Song, R Beyah
28th USENIX Security Symposium (USENIX Security 19), 1949-1966, 2019
Temporal multi-graph convolutional network for traffic flow prediction
M Lv, Z Hong, L Chen, T Chen, T Zhu, S Ji
IEEE Transactions on Intelligent Transportation Systems 22 (6), 3337-3348, 2020
Model-reuse attacks on deep learning systems
Y Ji, X Zhang, S Ji, X Luo, T Wang
Proceedings of the 2018 ACM SIGSAC conference on computer and communications …, 2018
Interpretable deep learning under fire
X Zhang, N Wang, H Shen, S Ji, X Luo, T Wang
29th {USENIX} Security Symposium ({USENIX} Security 20), 2020
Differentially private releasing via deep generative model (technical report)
X Zhang, S Ji, T Wang
arXiv preprint arXiv:1801.01594, 2018
Certchain: Public and efficient certificate audit based on blockchain for tls connections
J Chen, S Yao, Q Yuan, K He, S Ji, R Du
IEEE INFOCOM 2018-IEEE conference on computer communications, 2060-2068, 2018
Privacy risks of general-purpose language models
X Pan, M Zhang, S Ji, M Yang
2020 IEEE Symposium on Security and Privacy (SP), 1314-1331, 2020
Deepsec: A uniform platform for security analysis of deep learning model
X Ling, S Ji, J Zou, J Wang, C Wu, B Li, T Wang
2019 IEEE symposium on security and privacy (SP), 673-690, 2019
Graph data anonymization, de-anonymization attacks, and de-anonymizability quantification: A survey
S Ji, P Mittal, R Beyah
IEEE Communications Surveys & Tutorials 19 (2), 1305-1326, 2016
Structural data de-anonymization: Quantification, practice, and implications
S Ji, W Li, M Srivatsa, R Beyah
Proceedings of the 2014 ACM SIGSAC conference on computer and communications …, 2014
Sirenattack: Generating adversarial audio for end-to-end acoustic systems
T Du, S Ji, J Li, Q Gu, T Wang, R Beyah
Proceedings of the 15th ACM Asia Conference on Computer and Communications …, 2020
Label inference attacks against vertical federated learning
C Fu, X Zhang, S Ji, J Chen, J Wu, S Guo, J Zhou, AX Liu, T Wang
31st USENIX Security Symposium (USENIX Security 22), 1397-1414, 2022
Graph backdoor
Z Xi, R Pang, S Ji, T Wang
30th USENIX Security Symposium (USENIX Security 21), 1523-1540, 2021
On your social network de-anonymizablity: Quantification and large scale evaluation with seed knowledge.
S Ji, W Li, NZ Gong, P Mittal, RA Beyah
NDSS, 2015
Deep dual consecutive network for human pose estimation
Z Liu, H Chen, R Feng, S Wu, S Ji, B Yang, X Wang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
Trojaning language models for fun and profit
X Zhang, Z Zhang, S Ji, T Wang
2021 IEEE European Symposium on Security and Privacy (EuroS&P), 179-197, 2021
VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities.
X Duan, J Wu, S Ji, Z Rui, T Luo, M Yang, Y Wu
IJCAI, 4665-4671, 2019
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