Researchers Develop Feature-Aligned Speech Watermarking Method to Improve Robustness Against Reconstruction
Researchers have proposed a new audio watermarking technique that embeds identifiable information into speech while maintaining imperceptibility and improving resistance to suppression by speech reconstruction models. The method addresses a fundamental trade-off in existing watermarking designs by aligning watermarks with the original speech feature distribution, allowing higher watermark energy without reducing audio quality. This advancement is relevant for protecting audio content integrity and authenticity in applications where speech reconstruction or manipulation is a concern.
A team of researchers has introduced a feature-aligned watermarking approach for audio that tackles a longstanding challenge in the field: the robustness-fidelity trade-off. Traditional audio watermarking methods prioritize high fidelity and low energy to preserve perceptual quality, but this results in watermarks that are vulnerable to suppression by speech reconstruction models. The proposed method uses a pretrained speech codec to generate a pseudo-speech watermark and fuses it into the spectrogram of input audio, guided by voice activity detection (VAD) loss and perceptual losses to ensure embedding occurs within voiced regions. Experimental results demonstrate that the technique maintains imperceptibility comparable to existing approaches while substantially improving robustness against both known and unknown speech reconstruction models. The work was accepted for presentation at ICME 2026, a major conference in multimedia and signal processing.
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- arXiv cs.AICenter
Feature-Aligned Speech Watermarking for Robustness to Reconstruction Distortions
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