Steganalysis with CNN Using Multi-channels Filtered Residuals

Yuan, Yafei; Lu, Wei; Feng, Bingwen; Weng, Jian

doi:10.1007/978-3-319-68505-2_10

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…They improved the steganalytic performance of the network by adding the absolute layer to the first convolutional layer and by using the tanh activation function in the first two convolutional layers. Yuan et al used the same network structure as the initial CNN, but utilized three HPFs in a preprocessing stage [18]. ReST-Net [19] uses three different filter sets, namely 16 simplified linear SRM, 14 nonlinear SRM, and 16 Gabor filters (Figures 3 and 5) in the preprocessing stage to extract much more features from the input images.…”

Section: Cnn-based Image Steganalysismentioning

confidence: 99%

“…With the great success of convolutional neural networks (CNN) in object detection and recognition [15,16], using CNNs for steganalysis has been actively investigated [17][18][19][20][21][22][23][24][25][26][27]. Unlike handcrafted feature-based methods, a CNN can automatically extract and learn the features that are optimal or well suited for identifying steganographic methods.…”

Section: Introductionmentioning

confidence: 99%

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CNN-Based Ternary Classification for Image Steganalysis

Kang

Park

2019

Electronics

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This study proposes a convolutional neural network (CNN)-based steganalytic method that allows ternary classification to simultaneously identify WOW and UNIWARD, which are representative adaptive image steganographic algorithms. WOW and UNIWARD have very similar message embedding methods in terms of measuring and minimizing the degree of distortion of images caused by message embedding. This similarity between WOW and UNIWARD makes it difficult to distinguish between both algorithms even in a CNN-based classifier. Our experiments particularly show that WOW and UNIWARD cannot be distinguished by simply combining binary CNN-based classifiers learned to separately identify both algorithms. Therefore, to identify and classify WOW and UNIWARD, WOW and UNIWARD must be learned at the same time using a single CNN-based classifier designed for ternary classification. This study proposes a method for ternary classification that learns and classifies cover, WOW stego, and UNIWARD stego images using a single CNN-based classifier. A CNN structure and a preprocessing filter are also proposed to effectively classify/identify WOW and UNIWARD. Experiments using BOSSBase 1.01 database images confirmed that the proposed method could make a ternary classification with an accuracy of approximately 72%.

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Section: Cnn-based Image Steganalysismentioning

confidence: 99%