MDRSteg: large-capacity image steganography based on multi-scale dilated ResNet and combined chi-square distance loss

Abstract. Image steganography has emerged as a method of hiding secret data within an image file to ensure the security of the transmitted data. In this study, we propose an architecture named MDRSteg to unobtrusively hide a large-size image in another image based on a residual neural network with dilated convolution and multi-scale fusion. The architecture consists of an embedding network to hide the secret image in the cover-image and a revealing network to reveal the secret image from the stego-image, both networks are made up of fully convolutional residual modules. The networks are jointly trained with a loss function which is the combination of chi-square distance (CSD) and mean-square error. The proposed MDRSteg are trained and tested on three datasets, Labeled Faces in the Wild, Pascal visual object classes, and ImageNet. Extensive experiments have been done and the experimental results suggest that the proposed model can not only hide a large size image in another image with good invisibility and large hiding capacity (24 bits-per-pixel), but also exhibits good generalization ability. The experimental results also show that dilated convolution, multi-scale fusion, and combined CSD loss function have positive effects on the delicate image steganography results and proves that the model is practically useful for many applications.