Anchor Watermark: Robust Attribution for Diffusion-based Text-to-Audio Model

Abstract

With the increasing commercialization of the latent diffusion-based text-to-audio generation, model attribution has become a critical challenge. Embedding watermarks in generated audio is an effective way to distinguish synthetic from natural audio. However, existing watermarking methods often suffer from limited robustness or require additional training, limiting their scalability in practical applications. In this paper, we propose an anchor-based inversion optimization framework. The method embeds a watermark into the model