AI音乐伪影的傅里叶解释
A Fourier Explanation of AI-music Artifacts
AI总结：
本文分析了生成模型反卷积模块产生的系统性频率伪影，证明其源于模型架构，并据此提出一种简单可解释的AI音乐检测方法，在多场景下准确率超99%。
摘要：
生成式AI的快速崛起改变了音乐创作，数百万用户正在使用AI生成的音乐。尽管广受欢迎，但围绕版权侵权、职业替代和伦理影响的担忧引发了日益增多的审查和法律挑战。与此同时，AI检测服务相继出现，然而这些系统在很大程度上仍是不透明的且受私人控制的，这恰恰反映了它们旨在解决的同类问题。本文探讨了合成内容的根本特性及其检测方法。具体而言，我们分析了生成模型中常用的反卷积模块，并在数学上证明了其输出会表现出系统性的频率伪影——表现为微小但独特的频谱峰值。这种现象与著名的棋盘格伪影有关，被证明是所选模型架构固有的，而非训练数据或模型权重的结果。我们通过对开源模型以及Suno和Udio等商业AI音乐生成器进行大量实验，验证了我们的理论发现。我们利用这些见解，为AI生成音乐提出了一种简单且可解释的检测标准。尽管方法简单，但我们的方法实现了与基于深度学习的方法相当的检测准确率，在多种场景下准确率超过99%。
英文摘要：
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringement, job displacement, and ethical implications have led to growing scrutiny and legal challenges. In parallel, AI-detection services have emerged, yet these systems remain largely opaque and privately controlled, mirroring the very issues they aim to address. This paper explores the fundamental properties of synthetic content and how it can be detected. Specifically, we analyze deconvolution modules commonly used in generative models and mathematically prove that their outputs exhibit systematic frequency artifacts -- manifesting as small yet distinctive spectral peaks. This phenomenon, related to the well-known checkerboard artifact, is shown to be inherent to a chosen model architecture rather than a consequence of training data or model weights. We validate our theoretical findings through extensive experiments on open-source models, as well as commercial AI-music generators such as Suno and Udio. We use these insights to propose a simple and interpretable detection criterion for AI-generated music. Despite its simplicity, our method achieves detection accuracy on par with deep learning-based approaches, surpassing 99% accuracy on several scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
