Copy-move forgery detection using local tetra pattern based texture descriptor

doi:10.1007/s11042-022-14287-9

2023-01-18

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DOI：10.1007/s11042-022-14287-9

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摘要

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In modern era it has become increasingly easier to manipulate and tamper digital images, one of the primary reasons being the boon of commonplace availability of powerful image editing tools and software. These tools become a bane when used for malicious reasons as users can possibly add or remove important features from an image without leaving any obvious marks of tampering. Hence the need of forgery detection techniques which show high accuracy in detection arises. One of the most prevalent forms of image tampering is the copy-move forgery attack. In this type of forgery, a part of an image is copied and then pasted somewhere else in the same image with the intent to hide key features of the image. This paper introduces a new copy-move image forgery detection technique which relies on a texture feature descriptor called Local Tetra Pattern (LTrP) for block level image comparison used to localize tampered region(s). Initially, the input image is divided into overlapping blocks, then LTrP features are extracted from each block to form a single feature vector. Next, the feature vectors of all image blocks are sorted lexicographically, and then similar blocks are identified by matching the features from neighboring blocks. Finally, blocks matched falsely due to the presence of homogeneous color information like sea, field, and sky are removed using a shift vector aided outlier removal method. Experiments have been conducted on two standard datasets - GRIP and CoMoFoD. We have obtained 0.9834 and 0.9093 average F-1 scores at pixel-level for GRIP and CoMoFoD datasets respectively. The experimental results demonstrate that the proposed technique has been able to detect the forged regions with higher accuracy as compared to many state-of-the-art copy-move forgery detection methods. Moreover, experimental results on CoMoFoD dataset show that the method is able to correctly detect the forgery even after various post-processing attacks. The source code of proposed method is available at .

Keyword:

Copy-move forgery

Digital image forensics

GRIP

CoMoFoD

Duplicated region detection

Local tetra pattern