As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, despite the effective application of AI music generation (AIGM) tools, the unregulated use of them raises concerns about potential negative impacts on the music industry, copyright and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides an overview of existing AIGM detection methods. To lay a foundation to the general workings and challenges of AIGM detection, we first review general principles of AIGM, including recent advancements in deepfake audios, as well as multimodal detection techniques. We further propose a potential pathway for leveraging foundation models from audio deepfake detection to AIGM detection. Additionally, we discuss implications of these tools and propose directions for future research to address ongoing challenges in the field.

翻译：随着人工智能技术的持续演进，其在生成逼真且符合语境的內容方面的应用已扩展至多个领域。音乐作为一种艺术形式和娱乐媒介，深深植根于人类文化，正见证着AI在其创作过程中日益深入的参与。然而，尽管AI音乐生成工具得到了有效应用，其不受监管的使用引发了人们对音乐产业、版权和艺术完整性可能产生的负面影响的担忧，这凸显了有效进行AI生成音乐检测的重要性。本文综述了现有的AI生成音乐检测方法。为奠定AI生成音乐检测的一般工作原理与挑战的基础，我们首先回顾了AI音乐生成的一般原理，包括音频深度伪造技术的最新进展以及多模态检测技术。我们进一步提出了一条利用音频深度伪造检测中的基础模型来推进AI生成音乐检测的潜在路径。此外，我们讨论了这些工具的影响，并提出了未来研究方向，以应对该领域持续存在的挑战。