基于记忆增强 GAN 的异常检测

doi:10.11896/jsjkx.211000202

摘要/Abstract

摘要： 基于生成式对抗网络(Generative Adversarial Networks,GAN)的异常检测方法在训练阶段训练集仅由正常数据构成,当训练数据较为充分时,它在该训练集上能够取得较小的重构误差。然而在测试阶段,正常数据的重构误差和部分异常数据的重构误差之间的差别很小,使得基于GAN的异常检测方法的判别性能较差。为了解决该问题,提出了基于记忆增强GAN的异常检测方法。在基于GAN的异常检测方法中加入记忆增强模块,使模型能够记忆正常数据的特征,从而使得异常数据的重构误差变大,该方法的判别性能得到增强。在MNIST,Fashion-MNIST和CIFAR-10上的实验结果表明,与相关方法相比,所提方法具有更优的检测性能。

关键词: 异常检测, 生成式对抗网络, 记忆增强, MNIST

Abstract: In the training stage of the generative adversarial networks(GAN) based anomaly detection method,its training set consists of only normal data.When training data are sufficient,the GAN based anomaly detection method may obtain smaller reconstruction error.However,in the testing stage,the difference between the reconstruction errors of normal data and those of part novel data is too small,which makes the discriminant performance of the GAN based anomaly detection method become poor.To solve this problem,a memory-augmented GAN based anomaly detection method is proposed.A memory-augmented mo-dule is introduced into the proposed method to make it remember the characteristic of normal data.Hence,the reconstruction error of novel data becomes larger and thus the discriminant ability of the proposed method is enhanced.In comparison with the related approaches,experimental results on MNIST,Fashion-MNIST and CIFAR-10 verify that the proposed method has better detection performance.

Key words: Anomaly detection, Generative adversarial networks, Memory-augmented, MNIST

中图分类号:

TP391.4

周士金, 邢红杰. 基于记忆增强 GAN 的异常检测[J]. 计算机科学, 2022, 49(11A): 211000202-9. https://doi.org/10.11896/jsjkx.211000202

ZHOU Shi-jin, XING Hong-jieHebei. Memory-augmented GAN-based Anomaly Detection[J]. Computer Science, 2022, 49(11A): 211000202-9. https://doi.org/10.11896/jsjkx.211000202

参考文献

[1]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial net [C]//Neural Information Processing Systems.MIT Press,2014.
[2]RADFORD A,METZ L,CHINTALA S.Unsupervised repre-sentation learning with deep convolutional generative adversarial networks[J].arXiv:1511,06434,2016.
[3]CHEN D,YUE L,CHANG X,et al.NM-GAN:Noise-modulated generative adversarial network for video anomaly detection [J].Pattern Recognition,2021,116:107969.
[4]SIDDIQUI M A,STOKES J W,SEIFERT C,et al.Detecting cyber attacks using anomaly detection with explanations and expert feedback[C]//IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP 2019).IEEE,Brighton,UK,2019.
[5]GUI J,SUN Z,WEN Y,et al.A review on generative adversarial networks:Algorithms,theory,and applications[J].arXiv:2001.06937,2020.
[6]MIRZA M,OSINDERO S.Conditional generative adversarialnets [J].arXiv:1411.1784,2014.
[7]CHEN X,DUAN Y,HOUTHOOFT R,et al.Infogan:Interpretable representation learning by information maximizing gene-rative adversarial nets[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Barcelona,Spain,2016.
[8]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein gene-rative adversarial networks[C]//Proceedings of the Interna-tional Conference on Machine Learning.PMLR,Sydney,2017.
[9]MAO X,LI Q,XIE H,et al.Least squares generative adversarial networks[C]//Proceedings of the IEEE International Confe-rence on Computer Vision.Beijing,China,2017.
[10]BERTHELOT D,SCHUMM T,METZ L.Began:Boundaryequilibrium generative adversarial networks [J].arXiv:1703.10717,2017.
[11]DONAHUE J,KRÄHENBÜHL P,DARRELL T.Adversarialfeature learning [J].arXiv:1605.09782,2016.
[12]ZHU J Y,PARK T,ISOLA P,et al.Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice,Italy,2017.
[13]SCHLEGL T,SEEBÖCK P,WALDSTEIN S M,et al.Unsupervised anomaly detection with generative adversarial networks to guide marker discovery[C]//Proceedings of the International Conference on Information Processing in Medical Imaging.Springer:Boone,NC,USA,2017.
[14]ZENATI H,FOO C S,LECOUAT B,et al.Efficient gan-based anomaly detection [J].arXiv:1802.06222,2018.
[15]ZENATI H,ROMAIN M,FOO C S,et al.Adversarially learned anomaly detection [C]//Proceedings of the 2018 IEEE International conference on data mining(ICDM).IEEE,Sentosa,Singapore,2018.
[16]HOU Y,CHEN Z,WU M,et al.Mahalanobis distance based adversarial network for anomaly detection [C]//2020 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,Barcelona,Spain,2020.
[17]AKCAY S,ATAPOUR-ABARGHOUEI A,BRECKON T P.Ganomaly:Semi-supervised anomaly detection via adversarial training[C]//Proceedings of the Asian Conference on Computer Vision.Springer:Kyoto,2018.
[18]NGO P C,WINARTO A A,KOU C K L,et al.Fence GAN:Towards better anomaly detection[C]//31st International Confe-rence on Tools with Artificial Intelligence(ICTAI).IEEE,Portland,OR,USA,2019.
[19]AKÇAY S,ATAPOUR-ABARGHOUEI A,BRECKON T P.Skip-ganomaly:Skip connected and adversarially trained encoder-decoder anomaly detection[C]//proceedings of the 2019 International Joint Conference on Neural Networks(IJCNN).IEEE,Budapest,2019.
[20]HOCHREITER S,SCHMIDHUBER J.Long short-term memory [J].Neural Computation,1997,9(8):1735-80.
[21]SUKHBAATAR S,WESTON J,FERGUS R.End-to-end memo-ry networks[C]//Proceedings of the 28th International Confe-rence on Neural Information Processing Systems.Montreal,Ca-nada,2015.
[22]GULCEHRE C,CHANDAR S,CHO K,et al.Dynamic neural turing machine with continuous and discrete addressing schemes [J].Neural Computation,2018,30(4):857-884.
[23]MILLER A,FISCH A,DODGE J,et al.Key-value memory networks for directly reading documents [J].arXiv:1606.03126,2016.
[24]SANTORO A,BARTUNOV S,BOTVINICK M,et al.Meta-learning with memory-augmented neural networks [C]//Proceedings of the International Conference on Machine Learning.New York,USA,2016.
[25]GONG D,LIU L,LE V,et al.Memorizing normality to detectanomaly:Memory-augmented deep autoencoder for unsupervised anomaly detection [C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Long Beach,CA,USA,2019.
[26]PARK H,NOH J,HAM B.Learning memory-guided normality for anomaly detection[C]//Proceedings of IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.Seattle,WA,USA,2020.
[27]SALIMANS T,GOODFELLOW I,ZAREMBA W,et al.Im-proved techniques for training gans[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems.Barcelona,Spain,2016.
[28]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition [J].Proceedings of the IEEE,1998,86(11):2278-2324.
[29]XIAO H,RASUL K,VOLLGRAF R.Fashion-mnist:a novelimage dataset for benchmarking machine learning algorithms [J].arXiv:1708.07747,2017.
[30]KRIZHEVSKY A,HINTON G.Learning multiple layers of features from tiny images [J].Journal of Software Engineering and Applications,2009,11(2):1-60.
[31]FAWCETT T J P R L.An introduction to ROC analysis [J].Pattern Recognition Letters,2006,27(8):861-874.
[32]CAMPOS G O,ZIMEK A,SANDER J,et al.On the evaluation of unsupervised outlier detection:measures,datasets,and an empirical study [J].Data Mining Knowledge Discovery,2016,30(4):891-927.
[33]AHMED F,COURVILLE A.Detecting semantic anomalies[C]//Proceedings of the AAAI Conference on Artificial Intelligence.New York,2020.
[34]LIU F T,TING K M,ZHOU Z H.Isolation forest[C]//Proceedings of the 2008 Eighth IEEE International Conference on Data Mining.Pisa,Italy,2008.
[35]ZONG B,SONG Q,MIN M R,et al.Deep autoencoding gaussian mixture model for unsupervised anomaly detection[C]//Proceedings of the International Conference on Learning Representations.Vancouver,BC,Canada,2018.
[36]RUFF L,VANDERMEULEN R,GOERNITZ N,et al.Deepone-class classification [C]//Proceedings of the International Conference on Machine Learning.Stockholm,Sweden,2018.
[37]SCHLEGL T,SEEBÖCK P,WALDSTEIN S M,et al.fAno-GAN:Fast unsupervised anomaly detection with generative adversarial networks[J].Medical Image Analysis,2019,54:30-44.

相关文章 15

[1]	徐天慧, 郭强, 张彩明. 基于全变分比分隔距离的时序数据异常检测 Time Series Data Anomaly Detection Based on Total Variation Ratio Separation Distance 计算机科学, 2022, 49(9): 101-110. https://doi.org/10.11896/jsjkx.210600174
[2]	李其烨, 邢红杰. 基于最大相关熵的KPCA异常检测方法 KPCA Based Novelty Detection Method Using Maximum Correntropy Criterion 计算机科学, 2022, 49(8): 267-272. https://doi.org/10.11896/jsjkx.210700175
[3]	王馨彤, 王璇, 孙知信. 基于多尺度记忆残差网络的网络流量异常检测模型 Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network 计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011
[4]	杜航原, 李铎, 王文剑. 一种面向电商网络的异常用户检测方法 Method for Abnormal Users Detection Oriented to E-commerce Network 计算机科学, 2022, 49(7): 170-178. https://doi.org/10.11896/jsjkx.210600092
[5]	徐国宁, 陈奕芃, 陈一鸣, 陈晋音, 温浩. 基于约束优化生成式对抗网络的数据去偏方法 Data Debiasing Method Based on Constrained Optimized Generative Adversarial Networks 计算机科学, 2022, 49(6A): 184-190. https://doi.org/10.11896/jsjkx.210400234
[6]	武玉坤, 李伟, 倪敏雅, 许志骋. 单类支持向量机融合深度自编码器的异常检测模型 Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder 计算机科学, 2022, 49(3): 144-151. https://doi.org/10.11896/jsjkx.210100142
[7]	冷佳旭, 谭明圮, 胡波, 高新波. 基于隐式视角转换的视频异常检测 Video Anomaly Detection Based on Implicit View Transformation 计算机科学, 2022, 49(2): 142-148. https://doi.org/10.11896/jsjkx.210900266
[8]	代福芸, 迟静, 任明国, 张琪东. 几何特征和属性标签驱动的人脸图像合成 Face Image Synthesis Driven by Geometric Feature and Attribute Label 计算机科学, 2022, 49(10): 214-223. https://doi.org/10.11896/jsjkx.210900080
[9]	刘意, 毛莺池, 程杨堃, 高建, 王龙宝. 基于邻域一致性的异常检测序列集成方法 Locality and Consistency Based Sequential Ensemble Method for Outlier Detection 计算机科学, 2022, 49(1): 146-152. https://doi.org/10.11896/jsjkx.201000156
[10]	张叶, 李志华, 王长杰. 基于核密度估计的轻量级物联网异常流量检测方法 Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method 计算机科学, 2021, 48(9): 337-344. https://doi.org/10.11896/jsjkx.200600108
[11]	郭奕杉, 刘漫丹. 基于时空轨迹数据的异常检测 Anomaly Detection Based on Spatial-temporal Trajectory Data 计算机科学, 2021, 48(6A): 213-219. https://doi.org/10.11896/jsjkx.201100193
[12]	邢红杰, 郝忠. 基于全局和局部判别对抗自编码器的异常检测方法 Novelty Detection Method Based on Global and Local Discriminative Adversarial Autoencoder 计算机科学, 2021, 48(6): 202-209. https://doi.org/10.11896/jsjkx.200400083
[13]	管文华, 林春雨, 杨尚蓉, 刘美琴, 赵耀. 基于人体关节点的低头异常行人检测 Detection of Head-bowing Abnormal Pedestrians Based on Human Joint Points 计算机科学, 2021, 48(5): 163-169. https://doi.org/10.11896/jsjkx.200800214
[14]	刘立成, 徐一凡, 谢贵才, 段磊. 面向NoSQL数据库的JSON文档异常检测与语义消歧模型 Outlier Detection and Semantic Disambiguation of JSON Document for NoSQL Database 计算机科学, 2021, 48(2): 93-99. https://doi.org/10.11896/jsjkx.200900039
[15]	邹承明, 陈德. 高维大数据分析的无监督异常检测方法 Unsupervised Anomaly Detection Method for High-dimensional Big Data Analysis 计算机科学, 2021, 48(2): 121-127. https://doi.org/10.11896/jsjkx.191100141

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed