Run-length and edge statistics based approach for image splicing detection

Jing Dong, Wei Wang, Tieniu Tan, Yun Q. Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

64 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationDigital Watermarking - 7th International Workshop, IWDW 2008, Selected Papers
Number of pages12
StatePublished - 2009
Event7th International Workshop on Digital Watermarking, IWDW 2008 - Busan, Korea, Republic of
Duration: Nov 10 2008Nov 12 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5450 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Workshop on Digital Watermarking, IWDW 2008
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Characteristic functions
  • Edge detection
  • Image splicing
  • Run-length
  • Support vector machine (SVM)


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