JPEG steganalysis with combined dense connected CNNs and SCA-GFR

Jianhua Yang, Xiangui Kang, Edward K. Wong, Yun Qing Shi

    Research output: Contribution to journalArticle

    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.

    Original languageEnglish (US)
    Pages (from-to)8481-8495
    Number of pages15
    JournalMultimedia Tools and Applications
    Volume78
    Issue number7
    DOIs
    StatePublished - Apr 1 2019

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    Keywords

    • Adaptive steganography
    • Convolutional neural networks
    • Ensemble
    • JPEG image steganalysis

    ASJC Scopus subject areas

    • Software
    • Media Technology
    • Hardware and Architecture
    • Computer Networks and Communications

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