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Adversarial Robustness in Machine Learning Models has become a critical topic of research in recent years due to the susceptibility of machine learning models to attacks from malicious actors. Adversarial attacks involve making small, imperceptible changes to input data that can cause machine learning models to make incorrect predictions. This can have serious consequences in applications such as autonomous vehicles, healthcare, and cybersecurity.
In this thesis, the aim is to investigate strategies to improve the robustness of machine learning models against adversarial attacks. The objectives of the study include examining current literature on adversarial attacks and defenses, evaluating the effectiveness of different defense mechanisms, and proposing new techniques to enhance the security of machine learning models.
However, there are limitations to this study, including the complexity of adversarial attacks and the rapidly evolving nature of the field. The scope of the study will focus on specific types of adversarial attacks, such as image and text-based attacks, and will explore both white-box and black-box attack scenarios.
Table of Contents:
Chapter One: Introduction
1.1 Background
1.2 Problem Statement
1.3 Research Questions
1.4 Objectives of Study
1.5 Limitation of Study
1.6 Scope of Study
Chapter Two: Literature Review
2.1 Overview of Adversarial Attacks
2.2 Existing Defense Mechanisms
2.3 Evaluation of Defense Strategies
2.4 Recent Advances in Adversarial Robustness
Chapter Three: Research Methodology
3.1 Data Collection
3.2 Experimental Setup
3.3 Evaluation Metrics
3.4 Proposed Defense Techniques
Chapter Four: Discussion of Findings
4.1 Analysis of Experimental Results
4.2 Comparison of Defense Mechanisms
4.3 Discussion on the Efficacy of Proposed Techniques
Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion
Thesis Overview:
Adversarial Robustness in Machine Learning Models is a critical area of research that aims to enhance the security and reliability of machine learning systems against adversarial attacks. This thesis will investigate current literature on adversarial attacks and defenses, evaluate the effectiveness of different defense mechanisms, and propose new techniques to improve the robustness of machine learning models. The study will focus on specific types of adversarial attacks, such as image and text-based attacks, and will explore both white-box and black-box attack scenarios. The findings of this research will contribute to the field by providing insights into the vulnerabilities of machine learning models and offering strategies to mitigate the risks posed by adversarial attacks. Future research directions will also be discussed to further advance the field of adversarial robustness in machine learning models.
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