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Detecting Image Forgery with Color Phenomenology

Stanton, Jamie Alyssa

Abstract Details

2019, Master of Science (M.S.), University of Dayton, Electrical Engineering.
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.
Keigo Hirakawa (Advisor)
Vijayan Asari (Committee Member)
Temesgen Kebede (Committee Member)
22 p.

Recommended Citations

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)