Information Theoretic Detectability

Information theoretic detectability focuses on quantifying how easily a signal, event, or artifact can be distinguished from its background or other competing signals. Current research emphasizes developing methods to improve detectability in various applications, including anomaly detection in cybersecurity and industrial systems, identifying AI-generated text, and enhancing the visibility of small targets in image processing using techniques like sparse priors, model observer-inspired loss functions, and adversarial training. This research is crucial for improving the reliability and robustness of numerous systems, from autonomous vehicles to medical diagnostic tools, by addressing challenges in signal processing, machine learning, and security.