Deep residual learning for image steganalysis

• Published in: Multimedia tools and applications Vol. 77; no. 9; pp. 10437 - 10453
• Main Authors: Wu, Songtao, Zhong, Shenghua, Liu, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2018; Springer Nature B.V
• Subjects: Artificial neural networks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Digital imaging; Learning; Messages; Multimedia Information Systems; Neural networks; Special Purpose and Application-Based Systems; Steganography; Convolutional neural networks; Residual learning; Image steganalysis
• DOI: 10.1007/s11042-017-4440-4
• ISSN: 1380-7501

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.