An Improved Convolutional Neural Network for Steganalysis in the Scenario of Reuse of the Stego-Key

The topic of this paper is the use of deep learning techniques, more specifically convolutional neural networks, for steganalysis of digital images. The steganalysis scenario of the repeated use of the stego-key is considered. Firstly, a study of the influence of the depth and width of the convolution layers on the effectiveness of classification was conducted. Next, a study on the influence of depth and width of fully connected layers on the effectiveness of classification was conducted. Based on the conclusions from the studies, an improved convolutional neural network was created, which is characterized by the state-of-art level of classification efficiency but containing 20 times less parameters to learn during the training process. Smaller number of learnable parameters results in faster network learning, easier convergence, and smaller memory and computing power requirements. The paper contains description of the current state of art, description of the experimental environment, structures of the studied networks and the results of classification accuracy.