Abstract
With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.
Highlights
Digital imaging is applicable in many fields such as World Wide Web (WWW), print media, insurance industry, and surveillance security [1]
To analyze these changes we propose an efficient, simple, and robust descriptor, called DWT-dominant rotated local binary patterns (DRLBP) descriptor, which first decomposes chroma components of a given image into subbands using Discrete Wavelet Transform (DWT) and encodes these subbands using DRLBP [28] texture descriptor, which is a robust texture descriptor. e DWT-DRLBP descriptor of an image is passed to SVM for taking the decision whether it is authentic or spliced
Chroma components of a test image are decomposed into subbands using single level DWT to exploit splicing inconsistencies
Summary
Digital imaging is applicable in many fields such as World Wide Web (WWW), print media, insurance industry, and surveillance security [1]. Active techniques work on the phenomena that given image contains the information such as watermark or signature at the time of acquisition to ensure its authenticity. E use of these techniques is very limited due the nonavailability of information about the watermark or signature in most of the cases Due to this limitation passive techniques for splicing forgery detection are being developed, which do not depend on prior information. Our proposed technique measures the discontinuities that occurred in images due to splicing using the DWT subbands coefficients. Based on the proposed scheme which measures discontinuities and their coding, we introduce a new technique to detect splicing forgery by decomposing chroma components of a test image using DWT into subbands for measuring local discontinuities. The SVM is used to detect the image forgery in digital images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
