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).
Sanghoon KANG
Pukyong National University
Hanhoon PARK
Pukyong National University
Jong-Il PARK
Hanyang University
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Sanghoon KANG, Hanhoon PARK, Jong-Il PARK, "Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 2, pp. 350-353, February 2021, doi: 10.1587/transinf.2020EDL8116.
Abstract: 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).
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8116/_p
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@ARTICLE{e104-d_2_350,
author={Sanghoon KANG, Hanhoon PARK, Jong-Il PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets},
year={2021},
volume={E104-D},
number={2},
pages={350-353},
abstract={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).},
keywords={},
doi={10.1587/transinf.2020EDL8116},
ISSN={1745-1361},
month={February},}
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TY - JOUR
TI - Identification of Multiple Image Steganographic Methods Using Hierarchical ResNets
T2 - IEICE TRANSACTIONS on Information
SP - 350
EP - 353
AU - Sanghoon KANG
AU - Hanhoon PARK
AU - Jong-Il PARK
PY - 2021
DO - 10.1587/transinf.2020EDL8116
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2021
AB - 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).
ER -