基于对抗样本和自编码器的鲁棒异常检测

doi:10.11896/jsjkx.230300153

Abstract

Abstract: The anomaly detection method based on AutoEncoder only uses normal samples for training,so it can effectively reconstruct normal samples,but cannot reconstruct abnormal samples.In addition,when the anomaly detection method based on AutoEncoder is attacked by adversarial attacks,it often obtains wrong detection results.In order to solve the above problems,robust anomaly detection based on adversarial samples and AutoEncoder(RAD-ASAE) method is proposed.RAD-ASAE consists of two parameter-shared encoders and a decoder.First,the normal sample is perturbed slightly to generate the adversarial sample,and normal samples and adversarial samples are used to train the model at the same time to improve the adversarial robustness of the model.Second,the reconstruction error of the adversarial samples is minimized in the sample space,and the mean square error between the normal samples and the reconstructed samples of the adversarial samples is minimized.At the same time,the mean square error between the latent features of the normal samples and the adversarial samples is minimized in the latent space to improve the reconstruction ability of the AutoEncoder.Experimental results on MNIST,Fashion-MNIST and CIFAR-10 show that RAD-ASAE demonstrates better detection performance in comparison with 7 related methods.

Key words: AutoEncoder, Adversarial samples, Anomaly detection, Adversarial attack, Robustness

CLC Number:

TP391.4

LI Shasha, XING Hongjie. Robust Anomaly Detection Based on Adversarial Samples and AutoEncoder[J].Computer Science, 2024, 51(5): 363-373.

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Robust Anomaly Detection Based on Adversarial Samples and AutoEncoder

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