Image Copy Move Forgery Detection Based on Spatial feature Domain

Abstract

Copy-move forgery is a prevalent form of digital 
image manipulation where a segment of an image 
is duplicated and pasted within the same image to 
conceal information or create misleading content. 
Detecting such alterations is crucial for 
maintaining the integrity of digital images in fields 
like 
digital forensics, journalism, and legal 
investigations. This project explores spatial 
feature-based algorithms designed to identify 
copy-move forgery, highlighting the methods' key 
steps and unique characteristics.

Spatial feature-based detection focuses on the 
intrinsic properties of image pixels to identify 
repeated segments. The process typically involves 
three main stages: feature extraction, feature 
matching, and post-processing verification.  
Feature Extraction involves extracting unique 
attributes from an image that can help identify 
duplicated sections. Techniques like SIFT, SURF 
and ORB are commonly used to derive these 
features due to their robustness and versatility. 
Once features are extracted, the algorithm 
searches for patterns of similarity within the image, 
which might indicate duplicated regions. This is 
accomplished through various methods like KD
Trees, hashing, or clustering to find sets of similar 
features. After identifying possible matches, the 
algorithm applies further analysis to confirm 
whether these matches indeed represent a copy
move forgery. Techniques like RANSAC (Random 
Sample Consensus) and geometric transformations 
are employed to refine and validate the results. This 
project 
examines 
these 
algorithm’s 
effectiveness in detecting copy-move forgeries and 
discusses 
their 
resilience 
to 
common 
manipulations such as rotation, scaling, and 
compression. The study also addresses the 
computational demands of these methods, 
considering their applicability in real-world 
scenarios. By focusing on spatial feature-based 
algorithms, the project provides insights into 
current 
techniques for copy-move forgery 
detection, highlighting areas for improvement and 
future research.