Adversarial Attacks and Defenses in Deep Learning Systems - Naga Jyothi, Endluri Venkata (AG Volumes, 2024)
The rapidly expanding Internet applications of deep learning and related situations have led to a major increase in interest in adversarial attacks and defences in machine learning and deep neural networks. In this discipline, more and more researchers are employed. Give a thorough analysis of the ideas and approaches that make it possible for scholars to study adversarial attacks and defences. Furthermore, since there are no established assessment techniques, it is difficult to assess the true danger posed by adversarial assaults or the resilience of a deep learning model. Moreover, make an effort to provide the first analytical framework for a methodical comprehension of adversarial assaults. The framework is designed with cybersecurity in mind, including a lifecycle for hostile assaults and countermeasures. It was noted that no defence method now in use defeats hostile samples with both efficiency and efficacy.