Shuai Yuan
Orcid: 0009-0008-6575-5101Affiliations:
- University of Electronic Science and Technology of China, School of Computer Science and Engineering, Chengdu, China
According to our database1,
Shuai Yuan authored at least 16 papers
between 2021 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
FIGhost: Fluorescent Ink-Based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition.
IEEE Trans. Dependable Secur. Comput., 2026
No Trespassing: Ground-View Adversarial Patches for Privacy-Aware Management in COTS Robot Vacuum Cleaner.
IEEE Trans. Dependable Secur. Comput., 2026
MartDE: A Privacy-Preserving and Cost-Efficient Evaluation Framework for Data Marketplaces.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026
2025
FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition.
CoRR, May, 2025
Lightweight distributed deep learning on compressive measurements for internet of things.
Eng. Appl. Artif. Intell., 2025
Omni-Angle Assault: An Invisible and Powerful Physical Adversarial Attack on Face Recognition.
Proceedings of the Forty-second International Conference on Machine Learning, 2025
Proceedings of the IEEE International Conference on Communications, 2025
Proceedings of the IEEE International Conference on Communications, 2025
Proceedings of the 2025 IEEE Global Communications Conference, 2025
2024
Efficient and Privacy-Preserving Outsourcing of Gradient Boosting Decision Tree Inference.
IEEE Trans. Serv. Comput., 2024
CoRR, 2024
ITPatch: An Invisible and Triggered Physical Adversarial Patch against Traffic Sign Recognition.
CoRR, 2024
2023
Toward Efficient and End-to-End Privacy-Preserving Distributed Gradient Boosting Decision Trees.
Proceedings of the IEEE International Conference on Communications, 2023
2022
CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption.
Proceedings of the IEEE Global Communications Conference, 2022
2021
One radish, One hole: Specific adversarial training for enhancing neural network's robustness.
Peer-to-Peer Netw. Appl., 2021
Proceedings of the IEEE Global Communications Conference, 2021