Abstract
Recently, deep learning models based on convolutional neural networks (CNN) have been used in image steganalysis problems. In this paper, we present different architecture of CNN with dual tree complex wavelet transform for preprocessing before input images put into system. The main task of this transform is for exploiting the difference between cover and stego images through shift variance property. The net consists of five successive convolutions layers. Each one following by normalization and pooling layers ends with fully connected layer. The performance of system is evaluated through accuracy, precision, recall and f-score measures. The results show effectiveness of it with more than 0.9 precision values. HUGO, WOW and UNIWARD algorithms selected for implementation.
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