AI Watermarking 101: Tools and Techniques

In recent months, we've seen multiple news stories involving ‘deepfakes’, or AI-generated content: from images of Taylor Swift to videos of Tom Hanks and recordings of US President Joe Biden. Whether they are selling products, manipulating images of people without their consent, supporting phishing for private information, or creating misinformation materials intended to mislead voters, deepfakes are increasingly being shared on social media platforms. This enables them to be quickly propagated and have a wider reach and therefore, the potential to cause long-lasting damage.

In this blog post, we will describe approaches to carry out watermarking of AI-generated content, discuss their pros and cons, and present some of the tools available on the Hugging Face Hub for adding/detecting watermarks.

What is watermarking and how does it work?

Watermarking is a method designed to mark content in order to convey additional information, such as authenticity. Watermarks in AI-generated content can range from fully visible (Figure 1) to invisible (Figure 2). In AI specifically, watermarking involves adding patterns to digital content (such as images), and conveying information regarding the provenance of the content; these patterns can then be recognized either by humans or algorithmically.

There are two primary methods for watermarking AI-generated content: the first occurs during content creation, which requires access to the model itself but can also be more robust given that it is automatically embedded as part of the generation process. The second method, which is implemented after the content is produced, can also be applied even to content from closed-source and proprietary models, with the caveat that it may not be applicable to all types of content (e.g., text).