Copy-Move Forgery Detection Using Scale Invariant Feature and Reduced Local Binary Pattern Histogram

Jun Young Park,Il Kyu Eom,Yong Ho Moon,Tae An Kang

doi:10.3390/sym12040492

Jun Young Park, Il Kyu Eom + Show 2 more

Open Access

https://doi.org/10.3390/sym12040492

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Abstract

Because digitized images are easily replicated or manipulated, copy-move forgery techniques are rendered possible with minimal expertise. Furthermore, it is difficult to verify the authenticity of images. Therefore, numerous efforts have been made to detect copy-move forgeries. In this paper, we present an improved region duplication detection algorithm based on the keypoints. The proposed algorithm utilizes the scale invariant feature transform (SIFT) and the reduced local binary pattern (LBP) histogram. The LBP values with 256 levels are obtained from the local window centered at the keypoint, which are then reduced to 10 levels. For a keypoint, a 138-dimensional is generated to detect copy-move forgery. We test the proposed algorithm on various image datasets and compare the detection accuracy with those of existing methods. The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested copy-move forgery detection methods. Furthermore, the proposed method exhibits a uniform detection performance for various types of test datasets.

Highlights

Copy-move forgery (CMF) is a popular image tampering method, wherein a portion of an image is copied from one section of the image and is pasted elsewhere in the same image
The local binary pattern (LBP) value is obtained for every pixel in the 16 × 16 window centered on levels are reduced to levels, and their histogram is used as a new descriptor
The proposed copy-move forgery detection (CMFD) algorithm achieves the second rank with an ACC of 96.82

Summary

Introduction

Copy-move forgery (CMF) is a popular image tampering method, wherein a portion of an image is copied from one section of the image and is pasted elsewhere in the same image. It is important to verify the authenticity of the image and localize the copied and moved regions. Because the copied portion of an image is generally scaled or rotated, it is difficult to verify the authenticity of the image based on visual inspection alone. For this reason, the development of reliable copy-move forgery detection (CMFD) methods has become an important issue [1,2,3]. The common framework of CMFD comprises five steps: preprocessing (optional), feature extraction, matching, false match removal, and localization

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