Adversarial Attacks and Defense Mechanisms in Computer Vision: A Comprehensive Survey

Adversarial Attacks and Defense Mechanisms in Computer Vision: A Comprehensive Survey

연구 분야: Strategies

학회: African Conference on Research in Computer Science and Applied Mathematics

Adversarial attacks, which craft subtle input perturbations to induce failures in deep neural networks, pose critical threats to deployments of computer vision systems. This paper surveys recent advancements in adversarial attacks targeting computer vision systems across multiple domains, including image classification, object detection, semantic segmentation, and image-to-text models. This survey gives in-depth coverage of state-of-the-art defense strategies proposed recently to counter these attacks. Through rigorous evaluation of recent scholarly articles, this survey provides vital awareness into adversarial threats faced by vision systems and delivers clarity on open research frontiers essential for developing robust computer vision models and systems resilient to real-world attacks. Both problem analysis and defense strategy perspectives are covered in a holistic manner.