Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/549123
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dc.date.accessioned2024-03-05T07:13:29Z-
dc.date.available2024-03-05T07:13:29Z-
dc.identifier.urihttp://hdl.handle.net/10603/549123-
dc.description.abstractincluded newline-
dc.languageEnglish-
dc.rightsuniversity-
dc.titleMachine learning based passive image forgery classification using Hybrid- Handcrafted and CNN based features-
dc.creator.researcherAgarwal, Aanchal-
dc.subject.keyword2D-DCT, filtered residual.-
dc.subject.keyword3D-CNN, double-
dc.subject.keywordElectronics and Communication Engineering-
dc.subject.keywordEngineering-
dc.subject.keywordEngineering and Technology-
dc.subject.keywordEngineering Multidisciplinary-
dc.subject.keywordgeneral purpose detectors,-
dc.subject.keywordJPEG detection,-
dc.subject.keywordPassive image forensics,-
dc.contributor.guideKhandelwal, Vineet-
dc.publisher.placeNoida-
dc.publisher.universityJaypee Institute of Information Technology-
dc.publisher.institutionDepartment of Electronics and Communication Engineering-
dc.date.registered2018-
dc.date.completed2024-
dc.date.awarded2024-
dc.format.dimensionsxxiii, 168p., 19-Synopsis-
dc.format.accompanyingmaterialDVD-
dc.source.universityUniversity-
dc.type.degreePh.D.-
Appears in Departments:Department of Electronics and Communication Engineering

Files in This Item:
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01_title.pdfAttached File298.32 kBAdobe PDFView/Open
02_prelim pages.pdf850.81 kBAdobe PDFView/Open
03_table of contents.pdf216.69 kBAdobe PDFView/Open
04_abstract.pdf204.21 kBAdobe PDFView/Open
05_chapter1.pdf2.54 MBAdobe PDFView/Open
06_chapter2.pdf1.2 MBAdobe PDFView/Open
07_chapter3.pdf1.54 MBAdobe PDFView/Open
08_chapter4.pdf841.65 kBAdobe PDFView/Open
09_chapter5.pdf1.26 MBAdobe PDFView/Open
10_chapter6.pdf4.85 MBAdobe PDFView/Open
11_references.pdf1.09 MBAdobe PDFView/Open
12_list of publications.pdf258.93 kBAdobe PDFView/Open
13_synopsis.pdf1.25 MBAdobe PDFView/Open
80_recommendation.pdf391.98 kBAdobe PDFView/Open


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