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Exposing image splicing with inconsistent sensor noise levels

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Abstract

Splicing is a commonly used image tampering operation, where a part of one image is pasted into another image. The forged image can have completely different semantic from the original one and may mislead people in some serious occasions. To rebuild the credibility of the images, extensive forensic methods aiming to locate the spliced areas have been proposed in recent years. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement. However, most of the existing noise based methods are under the assumption that a synthetic additional white Gaussian noise (AWGN) is involved during the splicing. This maybe not the case in practice. In this study, we utilize the difference of the intrinsic sensor noise of the source images to expose the potential image splicing. In practice, the sensor noise level difference is common between images captured with different ISO settings. Through analyzing the characteristics of the sensor noise, a weighted noise level is proposed for reducing influences from image content thus can better localizing the splicing region. Specifically, the noise level of a questioned image is first estimated locally with principal component analysis (PCA)-based algorithm. Then, the estimated noise levels are weighted before clustering with k-means. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, not only for splicing localization purpose, but also for splicing detection purpose.

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Notes

  1. The code is available in https://github.com/zengh5/Exposing-splicing-sensor-noise

  2. The implementations of [12, 13, 21] are available in https://github.com/MKLab-ITI/image-forensics/tree/master/matlab_toolbox.

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Acknowledgements

The authors would like to thank Mr. M. D. Morteza for helping us in revising this manuscript. They would also like to thank the authors of [20] for collecting the codes for comparison study.

Funding

This work was supported by NSFC (Grant no. 61702429), the China Scholarship Council (Grant no. 201908515095), and the Research Fund for the Doctoral Program of Southwest University of Science and Technology (Grant no. 18zx7163). This work was partially done when Hui Zeng was a visiting scholar at Binghamton University, the State University of New York, New York, USA.

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Authors and Affiliations

  1. School of Computer Science & Technology, Southwest University of Science and Technology, Mianyang, China

    Hui Zeng & Anjie Peng

  2. School of Information Science & Engineering, Huaqiao University, Xiamen, China

    Xiaodan Lin

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  1. Hui Zeng
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  2. Anjie Peng
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  3. Xiaodan Lin
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Correspondence to Anjie Peng.

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Zeng, H., Peng, A. & Lin, X. Exposing image splicing with inconsistent sensor noise levels. Multimed Tools Appl 79, 26139–26154 (2020). https://doi.org/10.1007/s11042-020-09280-z

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