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DCU-Net: a dual-channel U-shaped network for image splicing forgery detection

  • S.I. : Machine Learning Applications for Security
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Abstract

The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.

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Acknowledgements

This work has been supported by National Key Research and Development Program of China. The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Wuhan University, Wuhan, China

    Hongwei Ding, Leiyang Chen, Qi Tao, Zhongwang Fu, Liang Dong & Xiaohui Cui

  2. Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan, China

    Hongwei Ding & Xiaohui Cui

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  1. Hongwei Ding
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Correspondence to Xiaohui Cui.

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Ding, H., Chen, L., Tao, Q. et al. DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. Neural Comput & Applic 35, 5015–5031 (2023). https://doi.org/10.1007/s00521-021-06329-4

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