Steganalysis versus splicing detection

Yun Q. Shi, Chunhua Chen, Guorong Xuan, Wei Su

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

38 Scopus citations


Aiming at detecting secret information hidden in a given image using steganographic tools, steganalysis has been of interest for years. In particular, universal steganalysis, not limited to attacking a specific steganographic tool, is of extensive interests due to its practicality. Recently, splicing detection, another important area in digital forensics has attracted increasing attention. Is there any relationship between steganalysis and splicing detection? Is it possible to apply universal steganalysis methodologies to splicing detection? In this paper, we address these intact and yet interesting questions. Our analysis and experiments have demonstrated that, on the one hand, steganography and splicing have different goals and strategies, hence, generally causing different statistical artifacts on images. However, on the other hand, both of them make the touched (stego or spliced) image different from the corresponding original (natural) image. Therefore, natural image model based on a set of carefully selected statistical features under the machine learning framework can be used for steganalysis and splicing detection. It is shown in this paper that some successful universal steganalytic schemes can make promising progress in splicing detection if applied properly. A more advanced natural image model developed from these state-of-the-art steganalysis methods is thereafter presented. Furthermore, a concrete implementation of the proposed model is applied to the Columbia Image Splicing Detection Evaluation Dataset, which has achieved an accuracy of 92%, indicating a significant advancement in splicing detection.

Original languageEnglish (US)
Title of host publicationDigital Watermarking - 6th International Workshop, IWDW 2007, Proceedings
Number of pages15
StatePublished - 2008
Event6th International Workshop on Digital Watermarking, IWDW 2007 - Guangzhou, China
Duration: Dec 3 2007Dec 5 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5041 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other6th International Workshop on Digital Watermarking, IWDW 2007

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Digital forensics
  • Natural image model
  • Splicing detection
  • Steganalysis
  • Steganography
  • Tampering detection


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