Indian Journal of Science and Technology

Abstract

Objective: To detect copy-move forgery from a given digital image by reducing false detection thus increasing precision rate and F1 score. It is also invariant to scaling, rotation, noise attack. Methods/Analysis: The paper describes an effective and novel method to detect the copy-move forgery detection using Singular Value Decomposition (SVD) and Scale-Invariant Feature Transform (SIFT) features. It divides the given image into equal sized blocks and applies keypoint detection and feature descriptor for each block of the image. Then again SVD is calculated from128 SIFT descriptor to detect the forgery part of the given image. In this approach, a correlation is calculated for different images under different attacks like scaling, rotation, and noise. Finding: The proposed system is tested using standard image data against various types of image attacks like scaling, rotation, noise, blur etc. From the result, it is found that the proposed system is robust and invariant to most of the image attacks. The proposed system easily detects the copied part of the image more efficiently compared to other block based method since SIFT feature is invariant to scaling, rotation, noise, blur etc. Novelty/Improvement: The proposed methods uses block based technique and apply SIFT and SVD technique for detecting forged region in the given image. False detection is reduced in the proposed system which increases the precision and F1 score.

Keywords: Copy-Move Forgery, Difference of Gaussian (DoG), Digital Forgery, Scale-Invariant Feature Transform (SIFT), Singular Value Decomposition (SVD), Tampered Image