A Survey on Adversarial Deep Learning in Computer Vision

Abstract

Adversarial deep learning has emerged as a critical area of research within computer vision, revealing both the vulnerabilities and resilience of modern neural networks. This study provides a comprehensive survey of adversarial attacks and corresponding defense mechanisms, bridging foundational concepts with recent advancements. The taxonomy of adversarial attacks is thoroughly examined, including white-box, limited-information, real-world, and modern attacks, each exploiting specific vulnerabilities in deep learning models. On the defense front, this paper explores an extensive range of strategies, from adversarial training and architectural modifications to certifiable robustness frameworks and semantic-level defenses, emphasizing their strengths and limitations. Despite significant progress, the dynamic and evolving nature of adversarial threats underscores the need for adaptive, human-centered solutions. Integrating robust defenses such as input reconstruction, ensemble-based models, and dynamic mechanisms highlights the field’s innovation, but challenges persist in achieving universal robustness and scalability. This survey emphasizes the importance of interdisciplinary collaboration, blending human perception, computational security, and explainable AI to design defenses that align with real-world complexities and ethical considerations. By critically analyzing current methodologies and proposing future directions, this paper contributes to advancing the security and reliability of computer vision applications. It advocates for a forward-thinking approach that balances technical sophistication with human-centric principles, ensuring that adversarial deep learning evolves into a discipline not just of technical defense, but also of trust, transparency, and accountability.