Hybrid deep learning and machine learning approach for passive image forensic

Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery det...

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Veröffentlicht in:IET image processing Jg. 14; H. 10; S. 1952 - 1959
Hauptverfasser: Thakur, Abhishek, Jindal, Neeru
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 21.08.2020
Schlagworte:
CMFD image manipulation dataset
deep neural network algorithm
DL algorithm
emerging methods
feature extraction
forged forged categories
forgeries
Gabor filters
image classification
image coding
image forensics
image segmentation
iris recognition
learning (artificial intelligence)
machine learning approach
machine learning‐based approach
neural nets
not forged categories
passive image forensic
passive image forgery detection
Special Section: Multidisciplinary advancement of imaging technologies: from medical diagnostics and genomics to cognitive machine vision, and artificial intelligence
machine learning approach
image classification
DL algorithm
CMFD image manipulation dataset
emerging methods
image forensics
deep neural network algorithm
passive image forgery detection
forged forged categories
feature extraction
image segmentation
forgeries
not forged categories
passive image forensic
iris recognition
Gabor filters
learning (artificial intelligence)
image coding
neural nets
machine learning-based approach
ISSN:1751-9659, 1751-9667
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Image forgery detection using traditional algorithms takes much time to find forgeries. The new emerging methods for the detection of image forgery use a deep neural network algorithm. A hybrid deep learning (DL) and machine learning-based approach is used in this study for passive image forgery detection. A DL algorithm classifies images into the forged and not forged categories, whereas colour illumination localises forgery. The simulated results are compared to other algorithms on public datasets. The simulated results achieved 99% accuracy for CASIA1.0, 98% accuracy for CASIA2.0, 98% accuracy for BSDS300, 97% accuracy for DVMM, and 99% accuracy for CMFD image manipulation dataset.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2019.1291