计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 211000202-9.doi: 10.11896/jsjkx.211000202
周士金, 邢红杰
ZHOU Shi-jin, XING Hong-jieHebei
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。
中图分类号:
[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. |
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