Multimedia Tools and Applications

, Volume 76, Issue 10, pp 12457–12479 | Cite as

Detecting Image Splicing Based on Noise Level Inconsistency

  • Heng Yao
  • Shuozhong Wang
  • Xinpeng Zhang
  • Chuan Qin
  • Jinwei Wang
Article

Abstract

In a spliced image, areas from different origins contain different noise features, which may be exploited as evidence for forgery detection. In this paper, we propose a noise level evaluation method for digital photos, and use the method to detect image splicing. Unlike most noise-based forensic techniques in which an AWGN model is assumed, the noise distribution used in the present work is intensity-dependent. This model can be described with a noise level function (NLF) that better fits the actual noise characteristics. NLF reveals variation in the standard deviation of noise with respect to image intensity. In contrast to denoising problems, noise in forensic applications is generally weak and content-related, and estimation of noise characteristics must be done in small areas. By exploring the relationship between NLF and the camera response function (CRF), we fit the NLF curve under the CRF constraints. We then formulate a Bayesian maximum a posteriori (MAP) framework to optimize the NLF estimation, and develop a method for image splicing detection according to noise level inconsistency in image blocks taking from different origins. Experimental results are presented to show effectiveness of the proposed method.

Keywords

Digital image forensics Image splicing detection Noise level function (NLF) Bayesian maximum a posteriori (MAP) 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61303203, 61525203, 61472235), the Natural Science Foundation of Shanghai, China (13ZR1428400), the Innovation Program of Shanghai Municipal Education Commission (14YZ087), the Program of Shanghai Dawn Scholar (14SG36), Shanghai Academic Research Leader (16XD1401200), the Open Project Program of the National Laboratory of Pattern Recognition (201600003), Shanghai Engineering Center Project of Massive Internet of Things Technology for Smart Home (GCZX14014), the PAPD Fund, and the CICAEET Fund.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Heng Yao
    • 1
  • Shuozhong Wang
    • 2
  • Xinpeng Zhang
    • 2
  • Chuan Qin
    • 1
  • Jinwei Wang
    • 3
  1. 1.Shanghai Key Lab of Modern Optical System, and Engineering Research Center of Optical Instrument and System, Ministry of EducationUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.School of Communication and Information EngineeringShanghai UniversityShanghaiChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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