Image copy-move forgery detection : new tools and techniques / Badal Soni, Pradip K. Das.
Soni, Badal, author.; Das, Pradip K. author.
2022
TA1637 .S66 2022
Available Online

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Image copy-move forgery detection : new tools and techniques / Badal Soni, Pradip K. Das.
Author
Soni, Badal, author.
ISBN
9789811690419 (electronic bk.)
9811690413 (electronic bk.)
9789811690402
9811690405
DOI
https://doi.org/10.1007/978-981-16-9041-9
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-981-16-9041-9 doi
Call Number
TA1637 .S66 2022
Dewey Decimal Classification
621.36/7
Summary
This book presents a detailed study of key points and block-based copy-move forgery detection techniques with a critical discussion about their pros and cons. It also highlights the directions for further development in image forgery detection. The book includes various publicly available standard image copy-move forgery datasets that are experimentally analyzed and presented with complete descriptions. Five different image copy-move forgery detection techniques are implemented to overcome the limitations of existing copy-move forgery detection techniques. The key focus of work is to reduce the computational time without adversely affecting the efficiency of these techniques. In addition, these techniques are also robust to geometric transformation attacks like rotation, scaling, or both.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Description based on print version record.
Added Author
Das, Pradip K. author.
Series
Studies in computational intelligence ; v. 1017.
Available in Other Form
IMAGE COPY-MOVE FORGERY DETECTION.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
Introduction
Background Study and Analysis
Copy-Move Forgery Detection using Local Binary Pattern Histogram Fourier Features
Blur Invariant Block-based CMFD System using FWHT Features
Geometric Transformation Invariant Improved Block based Copy-Move Forgery Detection
Key-points based Enhanced Copy-Move Forgery Detection System using DBSCAN Clustering Algorithm
Image Copy-Move Forgery Detection using Deep Convolutional Neural Networks.

Browse Subjects

Show more subjects...

Statistics

from
to
Export