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Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets
Sanghoon KANG Hanhoon PARK Jong-Il PARK
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2021/02/01
Online ISSN: 1745-1361
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).