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Title:A study on creativity: Detection and network structures
Author(s):Agarwal, Sakshi
Advisor(s):Varshney, Lav R.
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):creativity
deepfake
deepfake detection
networks
Abstract:In recent years, the topic of creativity has attracted extensive focus in the form of public discussion as well as research study. This has largely been in two areas: applying technology in order to innovate, as well as studying creativity in society and analyzing its dependence on social parameters and on network characteristics. Computational creativity has been used positively for automated creation of new content like art or recipes, but it is also being applied for pernicious activities like generating vile or misleading content, morphing pornographic or unethical videos/pictures to spread misinformation, or for blackmail. Such instances of fake content generated by artificial intelligence based generative techniques with potentially harmful applications are commonly referred to as deepfakes. This thesis consists of two parts that focus on each of these aspects separately. The first part deals with the detection problem for deepfake content. It outlines a classification problem for identifying an image as legitimate or fake, and obtains bounds on the expected performance while identifying fake content generated by generative adversarial networks. It further uses an approximation from Euclidean information theory for the low error regime and gives simplified bounds for the case where accuracy of the generative process is high. The second part deals with studying the effects of network parameters on creative productivity in social networks. It includes an overview of various theories on the ways by which network structure affects creativity, along with empirical results obtained by analyzing university innovation data alongside the online friendship networks for the same universities.
Issue Date:2019-04-24
Type:Text
URI:http://hdl.handle.net/2142/105083
Rights Information:Copyright 2019 Sakshi Agarwal
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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