Detecting Image Forgery with Color Phenomenology
Date of Award
M.S. in Electrical Engineering
Department of Electrical and Computer Engineering
Advisor: Keigo Hirakawa
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.
Electrical Engineering, Engineering, Image Forensics, Deep Learning, Convolutional Neural Network, CNN, Chromaticity, Multimedia Forensics
Copyright 2019, author
Stanton, Jamie Alyssa, "Detecting Image Forgery with Color Phenomenology" (2019). Graduate Theses and Dissertations. 6744.