Improved run length based detection of digital image splicing

Authors:
Zhongwei He;Wei Lu;Wei Sun
Affiliations:
School of Software, Sun Yat-sen University, Guangzhou, China;School of Information Science and Technology, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China;School of Software, Sun Yat-sen University, Guangzhou, China
Venue:
IWDW'11 Proceedings of the 10th international conference on Digital-Forensics and Watermarking
Year:
2011

Citing 5
Cited 0

A natural image model approach to splicing detection

Proceedings of the 9th workshop on Multimedia & security
Steganalysis Versus Splicing Detection

IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Run-Length and Edge Statistics Based Approach for Image Splicing Detection

Digital Watermarking
A bibliography on blind methods for identifying image forgery

Image Communication
Detection of image splicing based on hilbert-huang transform and moments of characteristic functions with wavelet decomposition

IWDW'06 Proceedings of the 5th international conference on Digital Watermarking

Quantified Score

Hi-index	0.00

Visualization

Abstract

Image splicing is very common and fundamental in image tampering, which severely threatens the integrity and authenticity of images. As a result, there is a great need for the detection of image splicing. In this paper, an improved run length based scheme is proposed to detect this specific artifact. Firstly, the edge gradient matrix of an image is computed. Secondly, approximate run length is defined and calculated along the edge gradient direction. Thirdly, features are constructed from the related histograms of the approximate run length. Finally, support vector machine (SVM) is exploited to classify the authentic and spliced images using the constructed features. The experiment results demonstrate that the proposed approach can achieve a moderate accuracy with far less computational cost and much fewer features when compared with a similar method.