ALGORITHMS FOR DETECTING IMAGE COPY MOVE FORGERY BASED ON SPATIAL FEATURE DOMAIN

Abstract

Image forgery detection, specifically copy-move forgery, remains a critical task in
digital forensics to ensure the integrity and authenticity of digital images. This paper presents
an investigation into copy-move forgery detection algorithms based on the spatial feature
domain. Copy-move forgery involves duplicating and pasting a region of an image to another
location within the same image, often to conceal or tamper with certain elements. In this
study, we explore various spatial feature-based techniques for detecting such forgeries,
leveraging properties such as texture, color, and geometric characteristics. We analyze the
effectiveness of different feature extraction methods and similarity measures in identifying
duplicated regions in images. Furthermore, we investigate the integration of machine learning
and deep learning approaches to enhance the detection performance. Experimental
evaluations on benchmark datasets demonstrate the efficacy of the proposed algorithms in
accurately detecting copy-move forgeries, highlighting their potential for application in realworld
forensic scenarios. Overall, this paper contributes to advancing the state-of-the-art in
image forgery detection by providing insights into spatial feature-based algorithms tailored
for detecting copy-move forgeries.