Research And Implementation Of RMB Counterfeiting Technology Based On Mobile Phone Images

The research on the authentication method of RMB images based on mobile phones is helpful to maintain the international reputation of the RMB,guarantee China's financial security,maintain the normal economic order of the society,and maintain the stability of the society,and promotes anti-counterfeit currency work,and it also helps the expansion of RMB authentication in mobile phones.It has important research significance and application prospects.Specific works are as follows.(1)The current study sets up a real and fake dataset of mobile RMB images.Through the analysis of the anti-counterfeiting parts of the RMB image of the mobile phone,five configurations of three types of mobile phones with four resolutions(Meizu16plus?4032?3024,Xiaomi 8?4032?3024,Xiaomi 8?4032?2268,Honor v20?4000?3000,Honor v20?2992?2992)are used to collect the data sets of four intaglio printing areas(National Emblem,Middle 100,Plum Blossom,Collar)of 100 real and fake RMB.In each authentication area under each configuration,there are 900 true and 900 false samples in the training set,300 true and 300 false samples in the verification set,and 300 true and 300 false samples in the test set.(2)A LR authentication method based on texture features and color weighting is proposed and implemented.The real and fake data set of mobile phone RMB images is composed of gravure printing areas and has rich texture features.Therefore,based on GLCM texture features,GGCM texture features,GLCM texture features and color weighting,GGCM texture features and color weighted LR four authentication methods are designed.These four methods all take second order statistics of the occurrence matrix(ASM,CON,IDM and ENT)as texture features and LR as a classifier,color weighting is all implemented by 1×1 convolution kernel.The experiment proves that the accuracy of the authentication method using only GLCM or GGCM texture features is less accurate,while accuracy is improved after adding color weighting.(3)A method of RMB image authentication based on color weighting and VGG16 is proposed and implemented.In the RMB image authentication method based on a universal deep convolutional network,four classical networks(VGG16,Inception?v3,Res Net50,Dense Net?121)are used for experimental analysis.The research proves that the convolutional neural network,as a feature extractor,has more excellent forgery detection performance than that based on texture features;the block5 of VGG16 has the highest performance when used as a feature extractor.Therefore,a method of RMB authentication based on color weighting and VGG16 was proposed in the later stage.It is found that the color weighting helps to improve the authentication performance of the RMB authentication method based on VGG16.(4)A BCNN RMB image authentication method based on color weighting and VGG16 is proposed and implemented.This section proposes a BCNN authentication method based on VGG16,and a BCNN method based on color weighting and VGG16.Both of the above methods use one channel of VGG16 to simulate two channels to build a BCNN.Color weighting is achieved by a convolutional layer with the shape [1,1,3,3] immediately after the input layer.The experiment proves that the BCNN model is better than the general convolutional neural network in various binary classification indicators;color weighting is also conducive to improving the performance of forgery detection.(5)The transplantation of mobile phone RMB image authentication model on mobile phone is realized.Through Google's open source keras framework,the authentication model is quantified into a lightweight model that can be called by the Android system.