Run-Length and Edge Statistics Based Approach for Image Splicing Detection

  • Jing Dong
  • Wei Wang
  • Tieniu Tan
  • Yun Q. Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5450)

Abstract

In this paper, a simple but efficient approach for blind image splicing detection is proposed. Image splicing is a common and fundamental operation used for image forgery. The detection of image splicing is a preliminary but desirable study for image forensics. Passive detection approaches of image splicing are usually regarded as pattern recognition problems based on features which are sensitive to splicing. In the proposed approach, we analyze the discontinuity of image pixel correlation and coherency caused by splicing in terms of image run-length representation and sharp image characteristics. The statistical features extracted from image run-length representation and image edge statistics are used for splicing detection. The support vector machine (SVM) is used as the classifier. Our experimental results demonstrate that the two proposed features outperform existing ones both in detection accuracy and computational complexity.

Keywords

image splicing run-length edge detection characteristic functions support vector machine (SVM) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jing Dong
    • 1
  • Wei Wang
    • 1
  • Tieniu Tan
    • 1
  • Yun Q. Shi
    • 2
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijing
  2. 2.Dept. of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

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