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Image splicing localization using noise distribution characteristic

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Abstract

Image splicing/compositing is common content tampering operation. In this work, we devote to improve the detection accuracy of the splicing/compositing attack for image, and propose an effective image splicing localization method based on the noise distribution characteristic in image. Firstly, the test image is divided into non-overlapping blocks by using an improved simple linear iterative clustering (SLIC) algorithm. Then block-wise local noise level estimation and noise distribution characteristic estimation are performed to generate distinguishing features. Utilizing the fact that image regions from different sources tend to have larger inter-class difference, the fuzzy c-means clustering is used to identify spliced regions. Compared to existing noise-based image splicing detection methods, experimental results on different datasets have shown that the proposed method has superior performance, especially when the noise difference between the spliced region and the original region is small. Moreover, the proposed method is robust for content-preserving manipulations.

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Acknowledgements

This work was supported by the National Major Research and Development Plan Program of China under Grant No.2016YFB1001004; the National Natural Science Foundation of China under Grant No.61772416 and No. 91646108; Shaanxi province technology innovation guiding fund project, No.2018XNCG-G-02. The foundation of the State Key Laboratory of Astronautic Dynamics.

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Authors and Affiliations

  1. Xi’an University of Technology, Xi’an, 710048, Shaanxi, China

    Depeng Zhang, Xiaofeng Wang, Meng Zhang & Jiaojiao Hu

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  1. Depeng Zhang
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  2. Xiaofeng Wang
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  3. Meng Zhang
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  4. Jiaojiao Hu
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Correspondence to Xiaofeng Wang.

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Zhang, D., Wang, X., Zhang, M. et al. Image splicing localization using noise distribution characteristic. Multimed Tools Appl 78, 22223–22247 (2019). https://doi.org/10.1007/s11042-019-7408-8

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