A passive approach for the detection of splicing forgery in digital images
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A passive approach for the detection of splicing forgery in digital images Navneet Kaur 1 & Neeru Jindal 1 & Kulbir Singh 1 Received: 9 August 2019 / Revised: 7 May 2020 / Accepted: 26 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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 lowfrequency sub-bands after applying one level DWT. Furthermore, low-frequency subband 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. Keywords Accuracy . Discrete wavelet transform . Local binary pattern . Markov features . Splicing forgery
* Kulbir Singh [email protected] Navneet Kaur [email protected] Neeru Jindal [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Digital images have become an essential portion of our day-to-day life since they provide prosperous information. Due to a large number of photo-editing software such as Adobe Photoshop, GNU Image Manipulation Program (GIMP), etc., digital images can be easily manipulated for the user’s interest [16]. For example, in the medical field, physicians make a diagnosis based on images. Since medical images deal with a large amount of money, these images get manipulated for claiming medical insurance [43–46]. So, it creates a necessity for advanced methods to determine the legitimacy and truthfulness of digital images used in law, military, science, medical, journalism, and other images of extreme importance. Intrusive (active) and Non-intrusive (passive) techniques are used to authenticate the images. In intrusive methods, the information is inserted into the image, for instance, digit
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