Open AccessBlur‐invariant copy‐move forgery detection technique with improved detection accuracy utilising SWT‐SVD
Author(s) -
Dixit Rahul,
Naskar Ruchira,
Mishra Swati
Publication year - 2017
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.0537
Subject(s) - artificial intelligence , singular value decomposition , computer vision , computer science , invariant (physics) , wavelet transform , edge detection , pattern recognition (psychology) , discrete wavelet transform , block (permutation group theory) , segmentation , wavelet , robustness (evolution) , stationary wavelet transform , image (mathematics) , mathematics , image processing , biochemistry , chemistry , geometry , mathematical physics , gene
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.