Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

In the last decade, deep learning (DL) has emerged as a dominant technique for solving challenging problems in various fields. Consequently, a large and growing body of literature with reports of investigations into DL-based methods for image-forgery detection, classification, and localization is av...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Pham, Nam Thanh, Park, Chun-Su
Format: Article
Language:English
Subjects:
Copy-move image
Deep learning
Digital imaging
Forgery
image forgery
Image recognition
Location awareness
Machine learning
spliced image
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the last decade, deep learning (DL) has emerged as a dominant technique for solving challenging problems in various fields. Consequently, a large and growing body of literature with reports of investigations into DL-based methods for image-forgery detection, classification, and localization is available. Owing to advances in DL, DL-based approaches have yielded promising results in image forgery detection and localization tasks. This survey provides a comprehensive list of state-of-the-art DL-based methods for image-forgery detection. Copy-move images and spliced images, two of the most popular types of forged images, were considered.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3241837