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Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets

Sanghoon KANG, Hanhoon PARK, Jong-Il PARK

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Summary :

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).

Publication
IEICE TRANSACTIONS on Information 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

Authors

Sanghoon KANG
  Pukyong National University
Hanhoon PARK
  Pukyong National University
Jong-Il PARK
  Hanyang University

Keyword