The Impact of Audio Watermarking on Audio Anti-Spoofing Countermeasures

• URL: http://arxiv.org/abs/2509.20736v1
• Date: Thu, 25 Sep 2025 04:34:39 GMT
• Title: The Impact of Audio Watermarking on Audio Anti-Spoofing Countermeasures
• Abstract summary: This paper presents the first study on the impact of audio watermarking on spoofing countermeasures.<n>We construct watermark-augmented training and evaluation datasets, named the Watermark-Spoofing dataset.<n>Experiments show that watermarking consistently degrades anti-spoofing performance, with higher watermark density correlating with higher EERs.
• Score: 5.250755043845844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents the first study on the impact of audio watermarking on spoofing countermeasures. While anti-spoofing systems are essential for securing speech-based applications, the influence of widely used audio watermarking, originally designed for copyright protection, remains largely unexplored. We construct watermark-augmented training and evaluation datasets, named the Watermark-Spoofing dataset, by applying diverse handcrafted and neural watermarking methods to existing anti-spoofing datasets. Experiments show that watermarking consistently degrades anti-spoofing performance, with higher watermark density correlating with higher Equal Error Rates (EERs). To mitigate this, we propose the Knowledge-Preserving Watermark Learning (KPWL) framework, enabling models to adapt to watermark-induced shifts while preserving their original-domain spoofing detection capability. These findings reveal audio watermarking as a previously overlooked domain shift and establish the first benchmark for developing watermark-resilient anti-spoofing systems. All related protocols are publicly available at https://github.com/Alphawarheads/Watermark_Spoofing.git

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