A lightweight unsupervised adversarial detector based on autoencoder and isolation forest

Author:

Liu HuiORCID,Zhao Bo,Guo Jiabao,Zhang Kehuan,Liu Peng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference40 articles.

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2. K. Simonyan, A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, in: Y. Bengio, Y. LeCun (Eds.), 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015.

3. Unauthorized AI cannot recognize me: Reversible adversarial example;Liu;Pattern Recognit.,2023

4. An enhanced AI-based network intrusion detection system using generative adversarial networks;Park;IEEE Internet Things J.,2023

5. G. Lovisotto, H. Turner, I. Sluganovic, M. Strohmeier, I. Martinovic, SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations, in: 30th USENIX Security Symposium, USENIX Security 2021, August 11-13, 2021, 2021, pp. 1865–1882.

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1. Reliability Evaluation of Advanced Metering Infrastructure Based on Isolation Forest Anomalies Detection;2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS);2024-02-21

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