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Stanton_Thesis (2)__final format approved LW 4-29-19.pdf (2.46 MB)
ETD Abstract Container
Abstract Header
Detecting Image Forgery with Color Phenomenology
Author Info
Stanton, Jamie Alyssa
ORCID® Identifier
http://orcid.org/0000-0002-7766-1451
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572
Abstract Details
Year and Degree
2019, Master of Science (M.S.), University of Dayton, Electrical Engineering.
Abstract
We propose a method that is designed to detect manipulations in images based on the phenomenology of color. Segmented regions of the image are converted to chromaticity coordinates and compared to the white point, D65. If an image had been manipulated, the chromaticity coordinates will have a shifted white point relative to D65, the accepted average white point. We classify the image forgery using a convolutional neural network using a histogram of relevant statistics that indicate the white point shift. We verify this using a real world data set to demonstrate its effectiveness.
Committee
Keigo Hirakawa (Advisor)
Vijayan Asari (Committee Member)
Temesgen Kebede (Committee Member)
Pages
22 p.
Subject Headings
Electrical Engineering
;
Engineering
Keywords
Image Forensics
;
Deep Learning
;
Convolutional Neural Network
;
CNN
;
Chromaticity
;
Multimedia Forensics
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Citations
Stanton, J. A. (2019).
Detecting Image Forgery with Color Phenomenology
[Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572
APA Style (7th edition)
Stanton, Jamie.
Detecting Image Forgery with Color Phenomenology.
2019. University of Dayton, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572.
MLA Style (8th edition)
Stanton, Jamie. "Detecting Image Forgery with Color Phenomenology." Master's thesis, University of Dayton, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton15574119887572
Chicago Manual of Style (17th edition)
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Document number:
dayton15574119887572
Download Count:
489
Copyright Info
© 2019, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.
Release 3.2.12