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ORCIDSecTL@AsiaCCS 2023: Melbourne, VIC, Australia
- Proceedings of the 2023 Secure and Trustworthy Deep Learning Systems Workshop, SecTL 2023, Melbourne, VIC, Australia, July 10-14, 2023. ACM 2023, ISBN 979-8-4007-0181-8
- Ziyao Liu
, Jiale Guo, Mengmeng Yang, Wenzhuo Yang, Jiani Fan, Kwok-Yan Lam:
Privacy-Enhanced Knowledge Transfer with Collaborative Split Learning over Teacher Ensembles. 1:1-1:13 - Zoe L. Jiang, Jiajing Gu, Hongxiao Wang, Yulin Wu, Junbin Fang, Siu-Ming Yiu, Wenjian Luo, Xuan Wang:
Privacy-Preserving Distributed Machine Learning Made Faster. 2:1-2:14 - Ye Sang, Yujin Huang, Shuo Huang, Helei Cui:
Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps. 3:1-3:9 - Zijian Wang, Shuo Huang, Yujin Huang, Helei Cui:
Energy-Latency Attacks to On-Device Neural Networks via Sponge Poisoning. 4:1-4:11 - Niklas Bunzel, Dominic Böringer:
Multi-class Detection for Off The Shelf transfer-based Black Box Attacks. 5:1-5:6 - Alka Luqman, Anupam Chattopadhyay, Kwok-Yan Lam:
Membership Inference Vulnerabilities in Peer-to-Peer Federated Learning. 6:1-6:5 - Yiming Qin, Jincheng Hu, Bang Wu:
Toward Evaluating the Robustness of Deep Learning Based Rain Removal Algorithm in Autonomous Driving. 7:1-7:7 - Md. Imran Hossen, Yazhou Tu, Xiali Hei:
A First Look at the Security of EEG-based Systems and Intelligent Algorithms under Physical Signal Injections. 8:1-8:8
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