Image Forgery Detection Using Singular Value Decomposition with Some Attacks

To improve the trustworthiness to assess the digital images by identifying authentic images and tampered images, this work is focused on Copy-Move based image Forgery Detection (CMFD) and classification using Improved Relevance Vector Machine (IRVM). In this paper, Biorthogonal Wavelet Transform wit...

Full description

Saved in:
Bibliographic Details
Published in:National Academy science letters Vol. 44; no. 4; pp. 331 - 338
Main Authors: Rathore, Neeraj Kumar, Jain, Neelesh Kumar, Shukla, Prashant Kumar, Rawat, UmaShankar, Dubey, Rachana
Format: Journal Article
Language:English
Published: New Delhi Springer India 01.08.2021
Springer Nature B.V
Subjects:
Decomposition
digital images
Digital imaging
exhibitions
extracts
Feature extraction
Forgery
genetic vectors
History of Science
Humanities and Social Sciences
image analysis
Image classification
Image manipulation
Machine learning
multidisciplinary
occurrence
Science
Science (multidisciplinary)
Short Communication
Similarity
Singular value decomposition
sorting
testing
wavelet
Wavelet transforms
Minkowski distance
Biorthogonal wavelet transform (BWT)
Copy-move forgery (CMF)
Glowworm swarm optimization (GSO)
Improved relevance vector machine (IRVM)
Similarity measure
ISSN:0250-541X, 2250-1754
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To improve the trustworthiness to assess the digital images by identifying authentic images and tampered images, this work is focused on Copy-Move based image Forgery Detection (CMFD) and classification using Improved Relevance Vector Machine (IRVM). In this paper, Biorthogonal Wavelet Transform with Singular Value Decomposition (BWT-SVD)-based feature extraction is applied to find the image forgery. The proposed method begins with dividing the test images into overlapping blocks, and then Biorthogonal Wavelet Transform (BWT) with Singular Value Decomposition (SVD) applies to extract the feature vector from the blocks. After that, the feature vectors are sorts and the duplicate vectors are identified by the similarity between two successive vectors. The occurrences of clone vectors are identified on the basis of Minkowski distance and the threshold value. Then, similarity criteria result in the existence of forgery in images. To classify images into the category of authentic images or forged images, improved version of Relevance Vector Machine (RVM) uses, which leads to efficiency and accuracy of forged image identification process. Performance of proposed scheme tests by performing experiments on CoMoFoD database. The simulation results show that the proposed IRVM scheme attained high performance when compared with existing Copy-Move based image Forgery Detection schemes in MATLAB environment.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0250-541X
2250-1754
DOI:10.1007/s40009-020-00998-w