International Journal of Computer Vision

, Volume 110, Issue 2, pp 202–221 | Cite as

Exposing Region Splicing Forgeries with Blind Local Noise Estimation

  • Siwei LyuEmail author
  • Xunyu Pan
  • Xing Zhang


Region splicing is a simple and common digital image tampering operation, where a chosen region from one image is composited into another image with the aim to modify the original image’s content. In this paper, we describe an effective method to expose region splicing by revealing inconsistencies in local noise levels, based on the fact that images of different origins may have different noise characteristics introduced by the sensors or post-processing steps. The basis of our region splicing detection method is a new blind noise estimation algorithm, which exploits a particular regular property of the kurtosis of nature images in band-pass domains and the relationship between noise characteristics and kurtosis. The estimation of noise statistics is formulated as an optimization problem with closed-form solution, and is further extended to an efficient estimation method of local noise statistics. We demonstrate the efficacy of our blind global and local noise estimation methods on natural images, and evaluate the performances and robustness of the region splicing detection method on forged images.


Blind local noise estimation Natural image statistics  Digital image forensics 



We would like to thank Daniel Zoran, Zhouchen Lin and Babak Mahdian for kindly sharing the images, codes and results of their works with us. We would also like to thank the two anonymous reviewers for their constructive comments that helped us improve this work. This work is supported in part by the National Science Foundation under Grant Nos. IIS-0953373, IIS-1208463 and CCF-1319800.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity at Albany, State University of New YorkAlbanyUSA

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