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Introduction:

Adversarial attacks have become a significant concern in the field of machine learning and artificial intelligence, as attackers can manipulate models to produce incorrect predictions by introducing small, carefully crafted perturbations to inputs. These attacks pose a threat to the integrity and security of AI systems, especially in critical applications such as autonomous driving and medical diagnosis. One approach to mitigating the impact of adversarial attacks is by developing methods for detecting when a model is presented with out-of-distribution (OOD) data, which can help in flagging potentially adversarial inputs before they can cause harm. This thesis aims to explore the use of adversarial robustness techniques for OOD detection, with the goal of improving the overall security and reliability of machine learning systems.

Masters Thesis Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objectives of the Study
1.4 Limitations of the Study
1.5 Scope of the Study

Chapter 2: Literature Review
2.1 Adversarial Attacks in Machine Learning
2.2 Adversarial Robustness Techniques
2.3 Out-of-Distribution Detection Methods
2.4 Related Work in Adversarial Robustness for OOD Detection

Chapter 3: Research Methodology
3.1 Data Collection and Preprocessing
3.2 Model Training and Evaluation
3.3 Adversarial Robustness Techniques Implementation
3.4 OOD Detection Metrics

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of Adversarial Robustness Techniques
4.2 Effectiveness of OOD Detection Methods
4.3 Analysis of Results
4.4 Comparison with Existing Approaches

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Future Research
5.4 Conclusion

Thesis Overview:

Adversarial attacks have emerged as a pressing issue in the field of machine learning, with the potential to cause serious harm in various applications. In response to this threat, researchers have developed adversarial robustness techniques to improve the resilience of machine learning models against such attacks. One important aspect of enhancing the security of AI systems is the ability to detect out-of-distribution (OOD) data, which could indicate the presence of adversarial inputs. This thesis aims to investigate the use of adversarial robustness methods for OOD detection, with the objective of enhancing the overall robustness and reliability of machine learning systems.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, and scope of the study. Chapter 2 presents a comprehensive review of the relevant literature on adversarial attacks, robustness techniques, OOD detection methods, and related work in the field. In Chapter 3, the research methodology is described, including data collection, model training, implementation of adversarial robustness techniques, and OOD detection metrics. Chapter 4 discusses the findings of the study, analyzing the performance of the proposed methods, evaluating the effectiveness of OOD detection, and comparing the results with existing approaches. Finally, Chapter 5 concludes the thesis, summarizing the key findings, discussing the contributions to the field, suggesting directions for future research, and providing a conclusion to the study. Through this research, we aim to advance the knowledge and understanding of adversarial robustness for OOD detection, and contribute to the development of more secure and reliable machine learning systems.

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