Techniques for embedding imperceptible or visible signals into digital content—such as text, images, audio, or AI-generated outputs—to assert provenance, ownership, or authenticity. AI watermarking approaches include statistical token-distribution biasing (for large language models) and frequency-domain embedding (for images), enabling detection of machine-generated content and supporting intellectual property protection and content provenance verification.
Semantic Classification
Content
- [Watermarking in the sand
- Kempner Institute (harvard.edu)](https://www.harvard.edu/kempner-institute/2023/11/09/watermarking-in-the-sand/)