Maskmark: Robust Neuralwatermarking for Real and Synthetic Speech
Patrick O’Reilly, Zeyu Jin, Jiaqi Su, Bryan Pardo
Abstract
High-quality speech synthesis models may be used to spread misinformation or impersonate voices. Audio watermarking can combat misuse by embedding a traceable signature in generated audio. However, existing audio watermarks typically demonstrate robustness to only a small set of transformations of the watermarked audio. To address this, we propose MaskMark, a neural network-based digital audio watermarking technique optimized for speech. MaskMark embeds a secret key vector in audio via a multiplicative spectrogram mask, allowing the detection of watermarked speech segments even under substantial signal-processing or neural network-based transformations. Comparisons to a state-of-the-art baseline on natural and synthetic speech corpora and a human subjects evaluation demonstrate MaskMark’s superior robustness in detecting watermarked speech while maintaining high perceptual transparency.