Blur‐invariant copy‐move forgery detection technique with improved detection accuracy utilising SWT‐SVD. Issue 5 (1st May 2017)

• Record Type: Journal Article
• Authors: Dixit, Rahul; Naskar, Ruchira; Mishra, Swati
• Is Part Of: IET image processing. Volume 11:Issue 5(2017)
• Page Start: 301
• Page End: 309
• Publication Date: 2017-05-01
• Subjects: edge detection -- image representation -- feature extraction -- image segmentation -- image restoration -- object detection -- image matching -- image colour analysis -- wavelet transforms -- singular value decomposition; blur‐invariant copy‐move forgery detection technique -- SWT‐SVD -- stationary wavelet transform -- singular value decomposition -- image block matching principle -- shift invariant -- feature extraction -- colour‐based segmentation concept -- blur invariance -- intelligent edge blurring; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0537
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Majority of the existing copy‐move forgery detection algorithms operate based on the principle of image block matching. However, such detection becomes complicated when an intelligent adversary blurs the edges of forged region(s). To solve this problem, the authors present a novel approach for detection of copy‐move forgery using stationary wavelet transform (SWT) which, unlike most wavelet transforms (e.g. discrete wavelet transform), is shift invariant, and helps in finding the similarities, i.e. matches and dissimilarities, i.e. noise, between the blocks of an image, caused due to blurring. The blocks are represented by features extracted using singular value decomposition (SVD) of an image. Also, the concept of colour‐based segmentation used in this work helps to achieve blur invariance. The authors' experimental results prove the efficiency of the proposed method in detection of copy‐move forgery involving intelligent edge blurring. Also, their experimental results prove that the performance of the proposed method in terms of detection accuracy is considerably higher compared with the state‐of‐the‐art.