Watermarking Scheme

Watermarking schemes aim to embed imperceptible signals into model outputs or datasets to verify ownership and detect unauthorized use, particularly crucial for protecting intellectual property in the age of powerful generative models like LLMs. Current research focuses on developing robust and undetectable watermarking techniques for various data types (text, tabular data, images) and model architectures, exploring both white-box (requiring model access) and black-box (only requiring output samples) approaches, with a strong emphasis on techniques resistant to adversarial attacks and model stealing. These advancements have significant implications for copyright protection, combating AI-generated misinformation, and ensuring responsible use of increasingly sophisticated AI technologies.