A Taxonomy of Watermarking Methods for AI-Generated Content

Abstract

As AI-generated content features more prominently in our lives, it becomes important to develop methods for tracing their origin. Watermarking is a promising approach, but a clear categorization of existing techniques is lacking. We propose a simple taxonomy of watermarking methods for generative AI based on where they are applied in the deployment of the models: (1) *post-hoc watermarking*, adding watermarks after content generation; (2) *out-of-model watermarking*, embedding watermarks during generation without modifying the model; (3) *in-model watermarking*, integrating watermarks directly into the model's parameters. By providing a structured overview of existing techniques across image, audio, and text domains, this taxonomy aims to help researchers, policymakers, and regulators make informed decisions about which approach best fits their needs, acknowledging that no single method is universally superior and that different approaches may be suited to specific use cases and requirements.