Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets

Sanghoon KANG  Hanhoon PARK  Jong-Il PARK  

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D   No.2   pp.350-353
Publication Date: 2021/02/01
Publicized: 2020/11/19
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDL8116
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
image steganalysis,  residual neural network,  hierarchical structure,  multi-class classification,  LSB,  PVD,  WOW,  S-UNIWARD,  

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Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).