Large scale JPEG steganalysis using hybrid deep learning framework
Deep learning frameworks have achieved overwhelming superiority in many fields of pattern recognition in recent years. However, the application of deep learning frameworks in image steganalysis is still in its initial stage. In this paper we firstly proved that the convolution phase and the quantization truncation phase are not learnable for deep neural networks. Then on the basis of the theoretical analysis, we proposed a new hybrid deep learning framework for JPEG steganalysis, which is made up of two hybrid parts. The first part is hand crafted. It corresponds to the the convolution phase and the quantization truncation phase of the rich models. The second part is a compound deep neural network containing three CNN subnets in which the model parameters are learnable during the training procedure. We have conducted extensive experiments on large scale dataset extracted from ImageNet. Primary dataset used in our experiments contains one million images, while our largest dataset contains ten million images. The large scale experiments show that our proposed framework outperforms all other steganalytic models (hand crafted or deeplearning based) in the literature. Furthermore, the experimental results revealed that our proposed framework possesses some great features, including well attacking target transfer ability and insensitive to altered JPEG block artifact.
ArXiv