A Feasibility Study of Data Poisoning against On-device Learning Edge AI by Physical Attack against Sensors
Author:
Affiliation:
1. College of Science and Engineering, Ritsumeikan University
2. Faculty of Science and Technology, Keio University
Publisher
Research Institute of Signal Processing, Japan
Link
https://www.jstage.jst.go.jp/article/jsp/28/4/28_107/_pdf
Reference7 articles.
1. [1] K. Yoshida and T. Fujino: Hardware Security on Edge AI Devices, IEEE ICE ESS Fundamentals Review, Vol. 15, No. 2, pp. 88-100, 2021 (in Japanese).
2. [2] K. Sunaga, K. Yoshida and H. Matsutani: Reliability Enhancement Techniques for On-Device Learning on Wireless Sensor Nodes, The Institute of Electronics, Information and Communication Engineers, Vol. 122, No. 328, pp. 29-34, 2023.
3. [3] J. Steinhardt, P. W. Koh and P. Liang: Certified Defenses for Data Poisoning Attacks, NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 3520-3532, 2017.
4. [4] B. Biggio, I. Corona, G. Fumera, G. Giacinto and F. Roli: Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks, Multiple Classifier Systems 10th International Workshop, pp.350-359, 2011.
5. [5] M. Tsukada, M. Kondo and H. Matsutani: A Neural Network-Based On-Device Learning Anomaly Detector for Edge Devices, IEEE Transactions on Computers, Vol. 69, No. 7, pp. 1027-1044, 2020.
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