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Traditional steganalysis methods generally include two steps: featureextraction and classification.A variety of steganalysis algorithms based on CNN(Convolutional Neural Network) have appeared in recent years. Among them, theconvolutional layer of the CNN model is usually used to extract steganographicfeatures, and the fully connected layer is used for classification. Because theeffectiveness of feature extraction seriously influences the accuracy ofclassification, designers generally improve the accuracy of steganographicdetection by improving the convolutional layer. For example, common optimizingmethods in convolutional layer include the improvement of convolution kernel,activation functions, pooling functions, network structures, etc. However, dueto the complexity and unexplainability of convolutional layers, it is difficultto quantitatively analyze and compare the effectiveness of feature extraction.Therefore, this paper proposes the variation coefficient to evaluate thefeature learning ability of convolutional layers. We select four typical imagesteganalysis models based CNN in spatial domain, such as Ye-Net, Yedroudj-Net,Zhu-Net, and SR-Net as use cases, and verify the validity of the variationcoefficient through experiments. Moreover, according to the variationcoefficient , a features modification layer is used to optimize the featuresbefore the fully connected layer of the CNN model , and the experimentalresults show that the detection accuracy of the four algorithms were improveddifferently.

Constructing feature variation coefficients to evaluate feature learning  capabilities of convolutional layers in steganographic detection algorithms  of spatial domain