Agnostic Watermarking

Agnostic watermarking aims to embed imperceptible identifiers into data generated by machine learning models, enabling verification of origin and detection of unauthorized use. Current research focuses on developing robust watermarking schemes for diverse model types, including deep neural networks, language models, and even tabular data, often employing techniques like error-correcting codes, diffusion model modifications, and hypothesis testing. This field is crucial for protecting intellectual property, combating misinformation, and ensuring the responsible deployment of increasingly powerful AI systems across various applications.