Generative AI systems are quickly improving, now able to produce believable output in several modalities including images, text, and audio. However, this fast development has prompted increased scrutiny concerning user privacy and the use of copyrighted works in training. A recent attack on machine-learning models called membership inference lies at the crossroads of these two concerns. The attack is given as input a set of records and a trained model and seeks to identify which of those records may have been used to train the model. On one hand, this attack can be used to identify user data used to train a model, which may violate their privacy especially in sensitive applications such as models trained on medical data. On the other hand, this attack can be used by rights-holders as evidence that a company used their works without permission to train a model. Remarkably, it appears that no work has studied the effect of membership inference attacks (MIA) on generative music. Given that the music industry is worth billions of dollars and artists would stand to gain from being able to determine if their works were being used without permission, we believe this is a pressing issue to study. As such, in this work we begin a preliminary study into whether MIAs are effective on generative music. We study the effect of several existing attacks on MuseGAN, a popular and influential generative music model. Similar to prior work on generative audio MIAs, our findings suggest that music data is fairly resilient to known membership inference techniques.

翻译：生成式人工智能系统正迅速发展，现已能够在图像、文本和音频等多种模态中生成可信的输出。然而，这种快速发展引发了对用户隐私及训练中使用受版权保护作品的日益关注。一种名为成员推理的机器学习模型攻击方法正处于这两大关注点的交汇处。该攻击以一组记录和一个训练好的模型作为输入，试图识别其中哪些记录可能被用于训练该模型。一方面，此攻击可用于识别用于训练模型的用户数据，这可能侵犯用户隐私，尤其是在医疗数据训练模型等敏感应用中。另一方面，权利持有人可利用此攻击作为证据，证明公司在未经许可的情况下使用其作品训练模型。值得注意的是，目前似乎尚无研究探讨成员推理攻击对生成式音乐的影响。鉴于音乐产业价值数十亿美元，且艺术家若能确定其作品是否被未经授权使用将获得利益，我们认为这是一个亟待研究的重要问题。因此，在本工作中，我们针对成员推理攻击对生成式音乐是否有效展开了初步研究。我们评估了多种现有攻击对MuseGAN——一个具有影响力且广泛使用的生成式音乐模型——的效果。与先前关于生成式音频成员推理攻击的研究类似，我们的研究结果表明，音乐数据对已知的成员推理技术具有相当的抵抗力。