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Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients

  • Zahra Moghaddasi
  • Hamid A. Jalab
  • Rafidah Md. Noor
Original Article

Abstract

Digital image forgery has significantly increased due to the rapid development of several tools of image manipulation. Based on the manipulation used to produce a tampered image, image forgery techniques can be characterized into three types: copy–move forgery, image splicing, and image retouching. Image splicing is achieved by adding regions from one image into another. This technique changes the content of the target image and causes variations in image features which are used to detect the forgery regions. In this study, an image splicing forgery detection method based on low-dimensional singular value decomposition of discrete cosine transform (DCT) coefficients has been presented. The suspicious input image is divided into multi-size blocks, and each block is transformed into 2D DCT. The DCT coefficients are calculated correspondingly to each block. The features from DCT are extracted using SVD algorithm. The roughness measure is calculated for the set of singular values obtained. Lastly, four types of statistical features—mean, variance, third-order moment skewness, and fourth-order moment kurtosis—are extracted from SVD features and are then arranged in a feature vector. Feature reduction has been applied by kernel principal component analysis. Finally, support vector machine is used to distinguish between the authenticated and spliced images. The proposed method was evaluated against three standard image datasets CASIA v1, DVMM v1, and DVMM v2. The proposed method shows an average detection accuracy of 97.15, 99.30, and 96.97 for DVMM v1, CASIA v1, and DVMM v2, respectively. These results outperform several current image splicing detection methods.

Keywords

Image splicing SVM Kernel PCA SVM DCT 

Abbreviations

AUC

Area under curve

CASIA

Chinese Academy of Sciences, Institute of Automation

CN

Condition number

DCT

Discrete cosine transform

DVMM

Digital video multi media

FN

False negative

FP

False positive

KPCA

Kernel principal component analysis

Log

Logarithm

SVD

Singular value decomposition

SVM

Support vector machine

TN

True negative

TP

True positive

TNR

True-negative rate

TPR

True-positive rate

YCbCr

Luma (Y) and two chrominance (CbCr) components color space

Notes

Acknowledgements

The authors wish to acknowledge the helpful comments of the anonymous reviewers which helped improve and clarify this manuscript. This project is supported by the Fundamental Research Grant Scheme (FRGS), Project: FP073-2015A, MHE, Malaysia.

Authors’ contributions

All authors contributed equally and significantly in writing this article and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this article.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyUniversity MalayaKuala LumpurMalaysia

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