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

Document Type

Conference Proceeding

Publication Date

12-1-2009

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. © Springer-Verlag Berlin Heidelberg 2009.

Identifier

78650323484 (Scopus)

ISBN

[3642044379, 9783642044373]

Publication Title

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics

External Full Text Location

https://doi.org/10.1007/978-3-642-04438-0_7

e-ISSN

16113349

ISSN

03029743

First Page

76

Last Page

87

Volume

5450 LNCS

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