Publication Date

Spring 2022

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Chris Pollett

Second Advisor

Robert Chun

Third Advisor

Mark Stamp


Machine learning, computer vision, image forensics, Generative Adversarial Network (GAN), noise residual spoofing, denoising


Noise residue detection in digital images has recently been used as a method to classify images based on source camera model type. The meteoric rise in the popularity of using Neural Network models has also been used in conjunction with the concept of noise residuals to classify source camera models. However, many papers gloss over the details on the methods of obtaining noise residuals and instead rely on the self- learning aspect of deep neural networks to implicitly discover this themselves. For this project I propose a method of obtaining noise residuals (“noiseprints”) and denoising an image, as well as a Generative model that can learn how to reproduce noise resembling a target digital camera model’s noise noiseprint. Applying a noiseprint generated by this model onto a denoised image will be able to fool a discriminating model into classifying the wrong digital camera model. To the best of my knowledge, this is the first work that will explicitly detail denoising methods and noiseprint generation in a 128 by 128 resolution for specific camera models and individual cameras for the goal of fooling a classification model.