Robust and Imperceptible Watermarking Framework for Generative Audio Models

The rapid development of generative audio models has raised concerns about copyright protection and traceability. To tackle these challenges, we first propose a robust and imperceptible watermarking framework embedded directly into the generative process. Our method embeds watermarks into the convolutional layers of the generative model, allowing the synthesis of watermarked audio without compromising quality. A trainable binary mask selectively modulates kernel weights, ensuring precise and efficient embedding. The framework incorporates a dedicated encoder-decoder architecture for accurate watermark embedding and extraction. A normalization step aligns the modified kernel weights with the original statistical properties to preserve the model's performance. Experimental results across multiple datasets demonstrate the method's robustness against common audio attacks. Additionally, the approach achieves high audio fidelity and near-perfect watermark recovery, offering a practical solution for traceable and secure audio synthesis.