通过训练数据水印，让音频生成模型自动生成可追溯内容。
Latent Watermarking of Audio Generative Models
- 直接在训练数据中嵌入水印，模型生成时自动携带。
- 检测准确率超75%，误报率低于千分之一，抗编辑能力强。
- 适合开源模型版权保护，识别未授权使用或微调行为。
音频生成模型的发展带来了负责任发布和滥用检测的新挑战。为此，我们提出一种在潜在空间对生成模型进行水印的方法，通过对其训练数据进行特定水印处理。经水印的模型生成的潜在表示，其解码输出可被高置信度检测，且不受解码方式影响。该方法无需后期添加水印，为开源模型提供了更安全的溯源方案，并有助于识别未遵守许可条款的衍生作品。实验表明，即使在对潜在生成模型进行微调后，生成内容仍能以超过75%的准确率被检测，误报率控制在10⁻³以下。
原文摘要 · Abstract (English)
The advancements in audio generative models have opened up new challenges in their responsible disclosure and the detection of their misuse. In response, we introduce a method to watermark latent generative models by a specific watermarking of their training data. The resulting watermarked models produce latent representations whose decoded outputs are detected with high confidence, regardless of the decoding method used. This approach enables the detection of the generated content without the need for a post-hoc watermarking step. It provides a more secure solution for open-sourced models and facilitates the identification of derivative works that fine-tune or use these models without adhering to their license terms. Our results indicate for instance that generated outputs are detected with an accuracy of more than 75% at a false positive rate of $10^{-3}$, even after fine-tuning the latent generative model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。