Detection of image splicing based on Hilbert-Huang transform and moments of characteristic functions with wavelet decomposition

Document Type

Conference Proceeding

Publication Date

1-1-2006

Abstract

Image splicing is a commonly used technique in image tampering. This paper presents a novel approach to passive detection of image splicing. In the proposed scheme, the image splicing detection problem is tackled as a two-class classification problem under the pattern recognition framework. Considering the high non-linearity and non-stationarity nature of image splicing operation, a recently developed Hilbert-Huang transform (HHT) is utilized to generate features for classification. Furthermore, a well established statistical natural image model based on moments of characteristic functions with wavelet decomposition is employed to distinguish the spliced images from the authentic images. We use support vector machine (SVM) as the classifier. The initial experimental results demonstrate that the proposed scheme outperforms the prior arts. © Springer-Verlag Berlin Heidelberg 2006.

Identifier

33845464635 (Scopus)

ISBN

[3540488251, 9783540488255]

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/10.1007/11922841_15

e-ISSN

16113349

ISSN

03029743

First Page

177

Last Page

187

Volume

4283 LNCS

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