Latent Watermarking of Audio Generative Models

Abstract

The advancements in audio generative models have opened up new challenges in their responsible disclosure and the detection of their misuse. To address this, watermarking techniques have been recently developed, enabling the detection of content generated by a deployed model. For such techniques to be useful, the watermark must resist typical modifications applied to the model or its outputs. The use case of an open-source model trained on proprietary data is challenging, as post-hoc watermarks can then be trivially removed. In response, we introduce a method that watermarks latent audio generative models by directly watermarking their training data. We show the method to be robust against a broad range of audio edits including filtering, compression or even to changing the model’s decoder, maintaining high detection rates with very few false positives. Interestingly, we show that even fine-tuning the model on another dataset can only significantly lower the detection rate at the cost of degrading the generation performance near the level of re-training the model without the protected training data.