Steganalysis of Intra Prediction Mode and Motion Vector-based Steganography by Noise Residual Convolutional Neural Network

Abstract

Abstract In this paper, we present a universal steganalysis method for both intra prediction mode and motion vector-based steganography based on deep learning. Since the embedding process is eventually reflected in the modification of pixel values in decoded frames, we design a Noise Residual Convolutional Neural Network (NR-CNN) from the perspective of the spatial domain, which is the first CNN-based approach for this subject. In NR-CNN, feature extraction and classification modules are integrated into a unified and trainable network framework. It automatically learns features and implements classification in a data-driven manner, which effectively solves the existing problems. Experimental results show that NR-CNN has better performance of steganalysis than the related method.