Adaptive Attack

Adaptive attacks, which leverage knowledge of a system's defenses to craft more effective adversarial examples, are a growing concern across various machine learning applications. Current research focuses on developing both stronger adaptive attacks and more robust defenses, often employing techniques like optimization-based methods, ensemble models, and self-supervised learning to improve detection and resilience. This research is crucial for ensuring the security and reliability of machine learning systems in high-stakes domains, such as healthcare, finance, and autonomous systems, where vulnerabilities to adaptive attacks can have significant consequences.