부산대학교 도서관

In recent years, we have observed the growing vulnerability of deep neural networks (DNNs) to adversarial attacks, challenging the forefronts of machine learning. Adver-sarial machine learning has emerged as a crucial research area to enhance the defenses of neural networks against such attacks, especially in mission-critical vision applications. The limitations and intensive resource requirements of existing defense strategies, such as adversarial training, have spawned a search for more efficient and effective defenses in an increasingly dependent, data-driven world. We propose patch-boosting, a low-cost, data augmentation-based defense that can achieve up to a 40% percent increase in accuracy against adversarial attacks. Patch-boosting not only enhances network performance against adversarial attacks but also supports low-accuracy networks in achieving more accurate predictions. This is a promising development in adversarial machine learning, offering a practical and scalable defense mechanism against adversarial attacks.