Multi-Targeted Backdoor: Indentifying Backdoor Attack for Multiple Deep Neural Networks-Reference-Cited by-同舟云学术

Multi-Targeted Backdoor: Indentifying Backdoor Attack for Multiple Deep Neural Networks

Published:2020-04-01 Issue:4 Volume:E103.D Page:883-887
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

KWON Hyun¹²,YOON Hyunsoo¹,PARK Ki-Woong³

Affiliation:

1. School of Computing, Korea Advanced Institute of Science and Technology

2. Department of Electrical Engineering, Korea Military Academy

3. Department of Computer and Information Security, Sejong University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E103.D/4/E103.D_2019EDL8170/_pdf

Reference11 articles.

1. [1] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol.61, pp.85-117, 2015. 10.1016/j.neunet.2014.09.003

2. [2] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” International Conference on Learning Representations, 2014.

3. [3] B. Biggio, B. Nelson, and P. Laskov, “Poisoning attacks against support vector machines,” Proc. 29th International Conference on Machine Learning, pp.1467-1474, 2012.

4. [4] T. Gu, B. Dolan-Gavitt, and S. Garg, “Badnets: Identifying vulnerabilities in the machine learning model supply chain,” arXiv preprint arXiv:1708.06733, 2017.

5. [5] C. Yang, Q. Wu, H. Li, and Y. Chen, “Generative poisoning attack method against neural networks,” arXiv preprint arXiv:1703.01340, 2017.

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