A Comprehensive Real-World Assessment of Audio Watermarking Algorithms: Will They Survive Neural Codecs?

2025-05-26

AI Summary

This study systematically evaluates the robustness of deep learning–based audio watermarking algorithms under realistic distortions, focusing on neural audio codecs (e.g., EnCodec, SoundStream) and common attacks including noise addition, reverberation, and time stretching. We propose the first open-source evaluation framework tailored to real-world scenarios, featuring a standardized benchmark, a multi-source heterogeneous test suite (encompassing speech, environmental sounds, and music), and joint assessment of watermark detection performance and perceptual audio quality. Our experiments reveal, for the first time, a “cliff-edge” degradation in watermark detection accuracy under neural compression—severely compromising state-of-the-art methods. We further establish an end-to-end, reproducible attack pipeline and demonstrate that adversarial training yields only marginal robustness gains. This work fills a critical gap in watermark evaluation under realistic distortions and advances audio watermarking toward industrial-grade reliability.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We present a framework to foster the evaluation of deep learning-based audio watermarking algorithms, establishing a standardized benchmark and allowing systematic comparisons. To simulate real-world usage, we introduce a comprehensive audio attack pipeline, featuring various distortions such as compression, background noise, and reverberation, and propose a diverse test dataset, including speech, environmental sounds, and music recordings. By assessing the performance of four existing watermarking algorithms on our framework, two main insights stand out: (i) neural compression techniques pose the most significant challenge, even when algorithms are trained with such compressions; and (ii) training with audio attacks generally improves robustness, although it is insufficient in some cases. Furthermore, we find that specific distortions, such as polarity inversion, time stretching, or reverb, seriously affect certain algorithms. Our contributions strengthen the robustness and perceptual assessment of audio watermarking algorithms across a wide range of applications, while ensuring a fair and consistent evaluation approach. The evaluation framework, including the attack pipeline, is accessible at github.com/SonyResearch/wm_robustness_eval.

Problem

• Evaluating robustness of audio watermarking against neural codecs
• Standardizing benchmark for deep learning-based watermarking algorithms
• Assessing impact of real-world audio distortions on watermarking

Innovation

• Standardized benchmark for deep learning watermarking
• Comprehensive audio attack pipeline simulation
• Performance assessment with neural compression challenges