Publication Date
8-2023
Date of Final Oral Examination (Defense)
7-3-2023
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Computer Science
Department
Computer Science
Supervisory Committee Chair
Francesca Spezzano, Ph.D
Supervisory Committee Member
Edoardo Serra, Ph.D.
Supervisory Committee Member
Yantian Hou, Ph.D.
Abstract
Misinformation and disinformation disguised in the form of fake news stories have garnered a lot of attention as of late largely because they confuse and often anger the public, leading to a less cohesive society for us all. In response to the ever-growing issue of fake news stories circulating on social media, researchers have crafted various solutions to predict the veracity of stories in hopes of catching illegitimate ones before they can spread. In response to this research area, a newer research area focused on attacking fake news detectors is forming. In this work we have built an adversarial text generator using T5 and reinforcement learning capable of attacking a vast array of fake news detection systems. This work focuses on attacking the state-of-the-art (SOTA) text-based fake news classifier, and we compare our performance directly with the SOTA text-based adversary. Our adversary is more effective than the current SOTA, but we continue to struggle to produce adversarial text that could avoid detection by some other system or human readers.
DOI
https://doi.org/10.18122/td.2140.boisestate
Recommended Citation
Underwood, Chandler, "Transformer Reinforcement Learning Approach to Attack Automatic Fake News Detectors" (2023). Boise State University Theses and Dissertations. 2140.
https://doi.org/10.18122/td.2140.boisestate