Deep learning based forgery detection in digital images for identifying copy move abnormalities

M. Syamala SaiSree; J. Sanjana; K. Srija; K. Vineela

Author(s)

M. Syamala SaiSree Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana, India

J. Sanjana Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana, India

K. Srija Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana, India

K. Vineela Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana, India

Abstract

Copy-move forgery is a prevalent form of image manipulation where a part of an image is duplicated and pasted onto another area, often to conceal or alter information. It's commonly encountered in digital media forensics, with applications in detecting tampered images, verifying authenticity, and ensuring integrity in legal and journalistic contexts. Currently, detecting copy-move forgery relies heavily on manual analysis by forensic experts. This process involves visually inspecting images, looking for inconsistencies in textures, lighting, and patterns. Despite its reliability, manual analysis is time-consuming and resource-intensive, limiting its scalability and efficiency in handling large datasets. Manual analysis suffers from subjectivity and human error, leading to potential inaccuracies in identifying forged regions. It's impractical for processing a vast number of images quickly, hindering its applicability in real-time scenarios. Additionally, the increasing sophistication of forgery techniques demands more robust and automated solutions. VGG 16, a convolutional neural network (CNN), offers a promising solution for automating copy-move forgery detection. Trained on extensive datasets, VGG 16 excels in feature extraction, enabling it to recognize patterns indicative of tampering with high accuracy. Its hierarchical architecture allows for the detection of both global and local inconsistencies in images, enhancing its versatility and effectiveness in identifying forged regions.

Publication Details

Page(s) 702-710

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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Keywords

feature extraction, Convolutional Neural Networks (CNNs), Forensic Techniques, Digital Media Forensics, VGG 16

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