The Technology Of Replacing Sensitive Information Of Oil And Gas Exploration Images Based On Deep Learning

In the fast-developing information age,data has become an intangible asset in various industries.However,with the frequent occurrence of data security issues in recent years,relevant government companies have paid great attention to encrypt or remove sensitive data.Min has become a focus of current research.Both encryption and desensitization are strategies for data security issues.Among them,data desensitization differs from encryption in that it needs to replace sensitive data with insensitive information without leaving any traces.However,most of the current data desensitization technology can only target text information.In response to this phenomenon,this paper selects the field of oil and gas exploration as the research object,focuses on the curve image data with sensitive information,and draws on the image based on deep learning according to the characteristics of the curve structure.The restoration method is to use image restoration technology to achieve desensitization of sensitive information in the image.Based on the introduction of traditional image restoration methods,this paper proposes two different deep learning-based image restoration models based on context encoder and door-hole convolution.The restoration model based on context encoder network is essentially a kind of selfencoding network.Improved,the repair method based on door hole convolution is designed according to the problems of partial convolution.After testing them separately,it is concluded that the traditional image repair method can only target the linear mask area,and the repair effect is poor;the context-based encoder only targets the image missing in the center area,but the repair effect is better,and the result is real The repair method based on the door hole convolution can repair the image damage of any shape,but the result is not as real as the repair method based on the context encoder,and the model is more complicated and the training is more time-consuming.All in all,this article uses a deep learning-based image restoration method to desensitize image sensitive information,which makes up for the current industry’s inability to desensitize image data.A good desensitization effect was achieved after detailed testing on oil and gas image data.Compared with the traditional method,the result proves that it can achieve more obvious effect and more reliable desensitization effect in the special data of curve image.