Machine learning based passive image forgery classification using Hybrid- Handcrafted and CNN based features

dc.contributor.guide	Khandelwal, Vineet
dc.creator.researcher	Agarwal, Aanchal
dc.date.accessioned	2024-03-05T07:13:29Z
dc.date.available	2024-03-05T07:13:29Z
dc.date.awarded	2024
dc.date.completed	2024
dc.date.registered	2018
dc.description.abstract	included newline
dc.format.accompanyingmaterial	DVD
dc.format.dimensions	xxiii, 168p., 19-Synopsis
dc.identifier.uri	http://hdl.handle.net/10603/549123
dc.language	English
dc.publisher.institution	Department of Electronics and Communication Engineering
dc.publisher.place	Noida
dc.publisher.university	Jaypee Institute of Information Technology
dc.rights	university
dc.source.university	University
dc.subject.keyword	2D-DCT, filtered residual.
dc.subject.keyword	3D-CNN, double
dc.subject.keyword	Electronics and Communication Engineering
dc.subject.keyword	Engineering
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	Engineering Multidisciplinary
dc.subject.keyword	general purpose detectors,
dc.subject.keyword	JPEG detection,
dc.subject.keyword	Passive image forensics,
dc.title	Machine learning based passive image forgery classification using Hybrid- Handcrafted and CNN based features
dc.type.degree	Ph.D.