Universal Detection

Universal detection aims to create robust methods for identifying various forms of artificially generated content, including deepfakes (audio and video), AI-generated images, and malicious code. Current research focuses on developing generalized models, often employing techniques like mixture-of-experts architectures and leveraging pre-trained models such as CLIP, to achieve high accuracy across diverse datasets and attack types. These advancements are crucial for mitigating the risks associated with increasingly sophisticated AI-generated content and enhancing the security and trustworthiness of machine learning systems in various applications.