Real World Attack

Real-world attacks target the vulnerabilities of machine learning (ML) and artificial intelligence (AI) systems, aiming to compromise their functionality or extract sensitive information through realistic adversarial examples. Current research focuses on developing robust detection methods and mitigation strategies, employing techniques like adversarial training, generative adversarial networks (GANs), graph neural networks (GNNs), and federated learning, often applied to specific domains such as cybersecurity, autonomous driving, and industrial control systems. Understanding and addressing these attacks is crucial for ensuring the reliability and security of AI systems across various applications, impacting both the development of more resilient models and the safety of real-world deployments.