Rapid Countermeasure

Rapid countermeasures research focuses on developing robust defenses against various attacks targeting machine learning models and systems, including adversarial examples, data poisoning, and spoofing. Current efforts concentrate on improving model architectures (e.g., using self-supervised learning, neural rejection techniques, and ensemble methods) and developing novel algorithms for anomaly detection and feature protection, often tailored to specific application domains like speech recognition or image generation. This research is crucial for enhancing the security and reliability of AI systems across diverse sectors, from cybersecurity and communication networks to healthcare and transportation.