On the Robustness of Audio Deepfake Detection under Audio Watermarking

2026-08-25

AI Summary

研究通过音频水印评估音频深度伪造检测系统的鲁棒性，使用自监督学习、CNN和GNN模型，并分析了不同数据集上的表现差异。

Abstract

Recent advances in generative audio models have enabled highly realistic synthetic speech, increasing the importance of reliable audio deepfake detection (ADD) systems. While prior studies have primarily focused on adversarially optimized perturbations, the robustness of ADD systems under realistic signal transformations remains insufficiently understood. In this work, we investigate the impact of audio watermarking on ADD systems by treating watermarking as a structured, non-adversarial perturbation rather than a conventional attack mechanism. Using a watermark-based evaluation framework built upon WavMark, we evaluate multiple self-supervised learning (SSL), Convolutional Neural Network (CNN) and Graph Neural Netrowk (GNN)-based ADD models across several benchmark datasets. Beyond conventional detection metrics, we further analyze watermark-induced representation shifts using Fréchet Distance, cosine similarity, and L2 distance in the embedding space. Experimental results reveal a strong dataset-dependent behavior: watermarking causes substantial performance degradation on ASVspoof 2021 LA and DF, while exhibiting limited impact on ASVspoof 2024, FoR, and ITW. Moreover, large embedding-space shifts are strongly associated with severe detection degradation, suggesting that watermark-induced perturbations can substantially alter the feature representations relied upon by current ADD systems. These findings demonstrate that benign signal transformations designed for content protection can expose previously overlooked robustness vulnerabilities in audio deepfake detection systems. Our code is available at https://github.com/ziqian0925/wm-ADD-robustness.git

Problem

• audio deepfake detection
• audio watermarking
• robustness

Innovation

• Audio Watermarking
• Deepfake Detection
• Robustness
• Self-supervised Learning
• Embedding Space Analysis