Adversarial Event

Adversarial events represent carefully crafted inputs designed to deceive machine learning models, leading to incorrect predictions or system failures. Current research focuses on understanding and mitigating these events across various domains, including autonomous vehicle navigation and malicious URL detection, with a particular emphasis on developing robust models and defense mechanisms against attacks targeting different data types (e.g., images, event streams, and labeled datasets). This research is crucial for enhancing the reliability and safety of AI systems in critical applications, addressing vulnerabilities that could have significant consequences in areas like cybersecurity and autonomous driving.