Research On Anomaly Detection Methods Based On Generative Adversarial Networks

Anomaly detection,as the name suggests,is the identification of data,events,or ob-servations that do not match the expected pattern.As a popular research field in the field of computer vision,anomaly detection has good application prospects in many fields.For example,information transmission detection in network security,material texture detec-tion related to manufacturing,and medical image detection in the medical field,etc.However,subject to the problems of time-consuming and labor-intensive data la-beling and data imbalance,the anomaly detection model based on supervised learning is difficult to meet predetermined expectations in some cases.In order to solve the above problems,this thesis will introduce a new unsupervised learning model that combines the idea of One-Class,that is,an anomaly detection model based on a generative adver-sarial network.The model recognizes anomalies by performing unsupervised learning on a single class of data without the need to label data in detail,avoiding the problem of data imbalance.It also uses the powerful fitting ability of network to generate data,and then distinguishes abnormal images or audio by comparing the distance between the reconstructed data and the test data.In short,the main research contents of this thesis are as follows:1.By introducing the Ano GAN model,the principle of the anomaly detection method based on the generative adversarial network is introduced in detail.And we pointed out the problems of the model,and on this basis,the two models of Efficient-GAN and GANomaly combining encoder and generative adversarial network are introduced.In addition,through the comparison of the experimental results of the above models,a point of view that the evaluation criteria of the images under different complexity should be inconsistent is proposed,and the above points are verified in subsequent experiments.2.Based on the above model,an improved model is proposed,that is,a pipeline model based on joint distribution.Compared with Ano GAN,the model solves the prob-lem of multiple optimizations in the inference stage,reduces time consumption,and uses joint distribution to improve model accuracy.In addition,the model extends the anomaly detection method in the image field to the audio field by adding a self-attention mecha-nism,which broadens the application range of the model.