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Table of Contents
1. INTRODUCTION
2. TAXONOMY OF MACHINE LEARNING AND ADVERSARIAL MODEL
- 2.1 Keywords and Definitions
- 2.2 Adversarial Threat Model
-- 2.2.1 The Attack Surface
-- 2.2.2 The Adversarial Capabilities
3. MOTIVATION AND CONTRIBUTION
4. ORGANIZATION
5. CONCLUSION
Summary
Deep learning has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems. However, security of deep learning systems are vulnerable to crafted adversarial examples. Different types of adversaries leverage these vulnerabilities to compromise deep learning systems. This paper provides a detailed discussion on various adversarial attacks with their countermeasures, analyzing different threat models and attack scenarios. The importance of this survey lies in summarizing recent advances in adversarial attacks in the field of deep learning, covering various attack types and defense strategies. The paper reviews recent findings on adversarial attacks, presents a taxonomy of related terms, and categorizes threat models. It discusses attack strategies, defense mechanisms, and concludes with the importance of addressing adversarial attacks in deep learning applications.