Golchubian, Arash

The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.

In this research, a new reputation-based model is utilized to disincentivize collusion
of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize
dishonest mining strategies in Blockchain. In the context of SDN, the model uses the
reputation values assigned to each entity to disincentivize collusion with an attacker. Our
analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased
model within a repeated game setting. In the context of Blockchain and mining,
we illustrate that by using the same socio-rational model, miners not only are incentivized
to conduct honest mining but also disincentivized to commit to any malicious activities
against other mining pools. We therefore show that honest mining strategies become Nash
Equilibrium in our setting.
This thesis is laid out in the following manner. In chapter 2 an introduction to
game theory is provided followed by a survey of previous works in game theoretic network
security, in chapter 3 a new reputation-based model is introduced to be used within the
context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution
concept is introduced to force cooperation by each mining entity in Blockchain, and finally,
in chapter 5, the concluding remarks and future works are presented.