A COMPREHENSIVE REVIEW OF ADVERSARIAL ATTACKS IN MACHINE LEARNING

Adversarial attacks pose a substantial risk to the security and dependability of machine learning (ML) models. These attacks entail creating harmful inputs, known as adversarial instances, which can lead models to provide inaccurate predictions. This article offers a thorough examination of adversarial assaults in machine learning, including their many forms, techniques of generation, current research, and potential future research areas. We analyze well-known attack techniques such as FGSM, DeepFool, Carlini & Wagner (C&W), and ZOO, emphasizing their advantages and constraints. The research limitations we have identified relate to the lack of comprehensive comparative analysis and the absence of a structured decision-making framework for offensive technique selection. In addition, we investigate the research obstacles related to adversarial variety, dynamic assault environments, the capacity to transfer knowledge across different domains, and the assessment of resilience in real-world scenarios. The paper highlights the need for investigating adversarial assaults to improve the resilience of models, enhance security measures, guide decision-making, stimulate innovation, and encourage responsible development of AI. In conclusion, we suggest potential areas for future study, such as the creation of improved defensive mechanisms, robust modeling tools, and the incorporation of multidisciplinary approaches.