research-article

A passive approach for the detection of splicing forgery in digital images

Authors:

Kulbir SinghAuthors Info & Claims

Multimedia Tools and Applications, Volume 79, Issue 43-44

Pages 32037 - 32063

https://doi.org/10.1007/s11042-020-09275-w

Published: 01 November 2020 Publication History

Abstract

With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches.

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Cited By

Bhowal ANeogy SNaskar R(2024)Deep Learning-based forgery detection and localization for compressed images using a hybrid optimization modelMultimedia Systems10.1007/s00530-024-01336-630:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00530-024-01336-6
Kaur NJindal NSingh K(2022)A deep learning framework for copy-move forgery detection in digital imagesMultimedia Tools and Applications10.1007/s11042-022-14016-282:12(17741-17768)Online publication date: 12-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-14016-2
Kaur NJindal NSingh K(2022)An improved approach for single and multiple copy-move forgery detection and localization in digital imagesMultimedia Tools and Applications10.1007/s11042-022-13105-681:27(38817-38847)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13105-6
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Information & Contributors

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Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 79, Issue 43-44

Nov 2020

1580 pages

ISSN:1380-7501

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2020

Accepted: 26 June 2020

Revision received: 07 May 2020

Received: 09 August 2019

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Cited By

Bhowal ANeogy SNaskar R(2024)Deep Learning-based forgery detection and localization for compressed images using a hybrid optimization modelMultimedia Systems10.1007/s00530-024-01336-630:3Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00530-024-01336-6
Kaur NJindal NSingh K(2022)A deep learning framework for copy-move forgery detection in digital imagesMultimedia Tools and Applications10.1007/s11042-022-14016-282:12(17741-17768)Online publication date: 12-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-14016-2
Kaur NJindal NSingh K(2022)An improved approach for single and multiple copy-move forgery detection and localization in digital imagesMultimedia Tools and Applications10.1007/s11042-022-13105-681:27(38817-38847)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11042-022-13105-6
Sushir RWakde D(2022)An improved detection of blind image forgery using hybrid deep belief network and adaptive fuzzy clusteringMultimedia Tools and Applications10.1007/s11042-022-12923-y81:20(29177-29205)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1007/s11042-022-12923-y
Singh GSingh K(2022)Chroma key foreground forgery detection under various attacks in digital video based on frame edge identificationMultimedia Tools and Applications10.1007/s11042-021-11380-381:1(1419-1446)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s11042-021-11380-3
Liu TYuan X(2021)A dual-tamper-detection method for digital image authentication and content self-recoveryMultimedia Tools and Applications10.1007/s11042-021-11179-280:19(29805-29826)Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11042-021-11179-2

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