JPEG steganalysis with combined dense connected CNNs and SCA-GFR

Document Type

Article

Publication Date

4-1-2019

Abstract

The detection of weakly hidden information in a JPEG compressed image is challenging. In this paper, we propose a 32-layer convolutional neural network (CNN) involving feature reuse by concatenating all features from previous layers. The proposed method can improve the flow of gradient, and the sharing of features and bottleneck layers can also dramatically reduce the number of parameters in the proposed CNN model. To further improve the detection accuracy and combine the directional features from the selection-channel-aware Gabor filtering residual (SCA-GFR) method with Gabor filtering and non-directional feature maps from the CNN model, an ensemble architecture called CNN-SCA-GFR is used, which combines the proposed CNN method with the conventional SCA-GFR method to detect J-UNIWARD and UERD. This can significantly reduce the detection error rate to below that of the existing JPEG steganalysis methods. For example, in the detection of J-UNIWARD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.67% lower than that achieved by XuNet, and 7.89% lower than that achieved by the conventional SCA-GFR method. When detecting UERD at 0.1 bpnzAC, the detection error rate using our proposed method is 5.94% lower than that achieved by XuNet, and 10.28% lower than that achieved by the conventional SCA-GFR method.

Identifier

85058620795 (Scopus)

Publication Title

Multimedia Tools and Applications

External Full Text Location

https://doi.org/10.1007/s11042-018-6878-4

e-ISSN

15737721

ISSN

13807501

First Page

8481

Last Page

8495

Issue

7

Volume

78

Grant

61772571

Fund Ref

National Natural Science Foundation of China

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