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Researcher: Halarnkar N Pallavi
Guide(s): Kekre H. B.
Keywords: Application of chaotic maps for robust image encryption
Image encryption using statistical distributions and MOD operator
Image scrambling in spatial domain
Image scrambling in transform domain
Image scrambling using transforms in different color spaces
Security and performance analysis
University: Narsee Monjee Institute of Management Studies
Completed Date: 29/06/2016
Abstract: In today s world one of most common mode of communication is Internet, a lot of newlinemultimedia data is exchanged over the Internet. This data may be in the form of newlinedigital images. As these images may be sometimes providing valuable information newlinethey need to be protected from unauthorized access. Digital images need to be protected not only over the communication medium of internet but sometimes personal identification details like scanned copies of pancard, Passport, ATM, credit card may be needed to store on one s own personal computer. The photos stored on one s mobile gallery, incase of mobile theft the images may be in wrong hands, in such cases, image security approach of scrambling and encryption may be used.In this thesis, new approaches to image scrambling and image encryption have newlinebeen proposed. The proposed techniques may be used to protect one s images on personal devices like mobile phones, laptops, tabs etc. The research work started with novel scramblers for spatial domain, namely RPrime shuffling, R-Prime shuffling on image blocks, R-Prime shuffling at bit level,Perfect shuffle and Kekre s Walsh sequency. The proposed scramblers were compared with existing scramblers and it was found that their performance was equivalent to existing scramblers. To add a layer of security, shuffling of pixels was carried out in transform domain. Transforms have a property of ecorrelating the pixels, one of the major goal of the scrambling techniques is to break the correlation of the pixels in the image so as to make it robust against any attacks and pixel prediction based on correlation. In this approach sinusoidal, non sinusoidal transforms, hybrid sinusoidal and hybrid non sinusoidal transforms were used and it was found that transform domain gave further reduction of correlation compared to spatial domain. To add a double layer of security to transform domain,color spaces were added. Different color spaces along with transforms were applied over the digital image and scrambling was carried out. newline
Appears in Departments:Department of Computer Engineering

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01_thesis cover page.pdfAttached File360.02 kBAdobe PDFView/Open
02_ack.pdf41.48 kBAdobe PDFView/Open
03_table of contents.pdf111.63 kBAdobe PDFView/Open
04_list of figures.pdf448.25 kBAdobe PDFView/Open
05_list of tables.pdf52.05 kBAdobe PDFView/Open
07_abstract.pdf42.33 kBAdobe PDFView/Open
08_chapter 1.1.pdf143.59 kBAdobe PDFView/Open
09_chapter2.pdf130.53 kBAdobe PDFView/Open
10_chapter 3.pdf3.38 MBAdobe PDFView/Open
12_chapter 4.pdf20.65 MBAdobe PDFView/Open
13_chapter 5.pdf60.51 MBAdobe PDFView/Open
14_chapter 6.pdf40.3 MBAdobe PDFView/Open
15_chapter 7.pdf14.96 MBAdobe PDFView/Open
16_chapter 8.pdf16.07 MBAdobe PDFView/Open
17_chapter 9.pdf12.41 MBAdobe PDFView/Open
18_chapter 10.pdf4.34 MBAdobe PDFView/Open
20_references.pdf217.2 kBAdobe PDFView/Open

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