Detection Methods against Digital Image Attacks for Secure Computer Vision

Abstract

"In today's digital age, our everyday life is filled with digital multimedia data as one of the primary forms for communication. As a result, Computer Vision (CV) systems supported by Machine Learning (ML) and Deep Learning (DL) techniques are now pervasive to process such multimedia. However, with modern technologies in sophisticated editing tools and DL models, it becomes a critical task to protect CV systems from digital image attacks. This thesis focuses on detecting a spectrum of digital attacks at the image level."