Detection Copy-Move Forgery in Image Via Quaternion Polar Harmonic Transforms

Abstract

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

A Review on Copy Move Forgery Detection in Document Images

“…Two kinds of forgery used in the literature are "Replication" and "Object Removal with uniform Background" types of forgeries. Later forgery technique becomes a problem for keypoint based methods.Salam A. Thaje et al [15]suggested the algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks.…”

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

“…During the initial identification of similar blocks, a possibility exists that false matches will be obtained. These false matches can be eliminated by using morphological operators [21,22] , random sample consensus, or window method. In keypoint-based CMFD algorithms, keypoints are extracted from the suspected image by using scaleinvariant feature transform [23,24] , speeded-up robust feature (SURF) [25,26] , and binary robust invariant scalable keypoints [17] .…”