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A multi-purpose image forensic method using densely connected convolutional neural networks

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Abstract

Multi-purpose forensics is attracting increasing attention worldwide. In this paper, we propose a multi-purpose method based on densely connected convolutional neural networks (CNNs) for simultaneous detection of 11 different types of image manipulations. An efficient CNN structure has been specifically designed for forensics by considering vital architecture components, including the number of convolutional layers, the size of convolutional kernels, the nonlinear activations, and the type of pooling layer. The dense connectivity pattern, which has better parameter efficiency than the traditional pattern, is explored to strengthen the propagation of features related to image manipulation detection. When compared with four state-of-the-art methods, our experiments demonstrate that the proposed CNN architecture can achieve better performance in multiple operation detections for different image sizes, especially on small image patches. Consequently, the proposed method can accurately detect local image manipulations. The proposed method can achieve better overall performance when tested on different databases as well as better robustness against JPEG compression even under low-quality JPEG compression.

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Acknowledgements

This work was supported by NSFC (Grant nos. U1536204, 61772571, 61702429).

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Authors and Affiliations

  1. Guangdong Key Lab of Information Security, Sun Yat-sen University, Guangzhou, 510006, China

    Yifang Chen & Xiangui Kang

  2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, USA

    Yun Q. Shi

  3. Department of Electrical and Computer Engineering, University of British Colombia, Vancouver, Canada

    Z. Jane Wang

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  1. Yifang Chen
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  2. Xiangui Kang
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  3. Yun Q. Shi
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  4. Z. Jane Wang
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Correspondence to Xiangui Kang.

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Chen, Y., Kang, X., Shi, Y.Q. et al. A multi-purpose image forensic method using densely connected convolutional neural networks. J Real-Time Image Proc 16, 725–740 (2019). https://doi.org/10.1007/s11554-019-00866-x

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