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Detection of double JPEG compression using modified DenseNet model

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Abstract

With the increasing tendency of the tempering of JPEG images, development of methods detecting image forgery is of great importance. In many cases, JPEG image forgery is usually accompanied with double JPEG compression, leaving no visual traces. In this paper, a modified version of DenseNet (densely connected convolutional networks) is proposed to accomplish the detection task of primary JPEG compression among double compressed images. A special filtering layer in the front of the network contains typically selected filtering kernels that can help the network following to discriminating the images more easily. As shown in the results, the network has achieved great improvement compared to the-state-of-the-art method especially on the classification accuracy among images with lower quality factors.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grants (U1536109, U1636206, 61525203, 61373151, 61472235).

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Authors and Affiliations

  1. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, China

    Ximei Zeng, Guorui Feng & Xinpeng Zhang

  2. School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China

    Ximei Zeng, Guorui Feng & Xinpeng Zhang

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  1. Ximei Zeng
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  2. Guorui Feng
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  3. Xinpeng Zhang
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Correspondence to Guorui Feng.

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Zeng, X., Feng, G. & Zhang, X. Detection of double JPEG compression using modified DenseNet model. Multimed Tools Appl 78, 8183–8196 (2019). https://doi.org/10.1007/s11042-018-6737-3

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