Membership and Dataset Inference Attacks on Large Audio Generative Models

• URL: http://arxiv.org/abs/2512.09654v1
• Date: Wed, 10 Dec 2025 13:50:00 GMT
• Title: Membership and Dataset Inference Attacks on Large Audio Generative Models
• Authors: Jakub Proboszcz, Paweł Kochanski, Karol Korszun, Donato Crisostomi, Giorgio Strano, Emanuele Rodolà, Kamil Deja, Jan Dubinski,
• Abstract summary: Generative audio models are often trained on vast corpora of artistic and commercial works.<n>A central question is whether one can reliably verify if an artist's material was included in training, thereby providing a means for copyright holders to protect their content.<n>In this work, we investigate the feasibility of such verification through membership inference attacks on open-source generative audio models.
• Score: 17.763094810756247
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative audio models, based on diffusion and autoregressive architectures, have advanced rapidly in both quality and expressiveness. This progress, however, raises pressing copyright concerns, as such models are often trained on vast corpora of artistic and commercial works. A central question is whether one can reliably verify if an artist's material was included in training, thereby providing a means for copyright holders to protect their content. In this work, we investigate the feasibility of such verification through membership inference attacks (MIA) on open-source generative audio models, which attempt to determine whether a specific audio sample was part of the training set. Our empirical results show that membership inference alone is of limited effectiveness at scale, as the per-sample membership signal is weak for models trained on large and diverse datasets. However, artists and media owners typically hold collections of works rather than isolated samples. Building on prior work in text and vision domains, in this work we focus on dataset inference (DI), which aggregates diverse membership evidence across multiple samples. We find that DI is successful in the audio domain, offering a more practical mechanism for assessing whether an artist's works contributed to model training. Our results suggest DI as a promising direction for copyright protection and dataset accountability in the era of large audio generative models.

Related papers

• Assessing the Effectiveness of Membership Inference on Generative Music [0.0]
  We study the effect of several existing attacks on MuseGAN, a popular and influential generative music model.<n>Similar to prior work on generative audio MIAs, our findings suggest that music data is fairly resilient to known membership inference techniques.
  arXiv  Detail & Related papers  (2025-12-25T18:54:16Z)
• UALM: Unified Audio Language Model for Understanding, Generation and Reasoning [124.19449187588832]
  Unified Audio Language Model (UALM) aims to unify audio understanding, text-to-audio generation, and multimodal reasoning in a single model.<n>We first present UALM-Gen, a text-to-audio language model that directly predicts audio tokens and is comparable to state-of-the-art diffusion-based models.<n>We present UALM-Reason, a multimodal reasoning model that utilizes both text and audio in the intermediate thinking steps to facilitate complex generation tasks.
  arXiv  Detail & Related papers  (2025-10-13T22:55:01Z)
• Pretrained Conformers for Audio Fingerprinting and Retrieval [0.0]
  We train conformer-based encoders that are capable of generating unique embeddings for small segments of audio.<n>We achieve state-of-the-art results for audio retrieval tasks while using only 3 seconds of audio to generate embeddings.
  arXiv  Detail & Related papers  (2025-08-15T17:19:09Z)
• Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners [69.70590867769408]
  Video and audio content creation serves as the core technique for the movie industry and professional users. Existing diffusion-based methods tackle video and audio generation separately, which hinders the technique transfer from academia to industry. In this work, we aim at filling the gap, with a carefully designed optimization-based framework for cross-visual-audio and joint-visual-audio generation.
  arXiv  Detail & Related papers  (2024-02-27T17:57:04Z)
• AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining [46.22290575167155]
  This paper proposes a framework that utilizes the same learning method for speech, music, and sound effect generation. Our framework introduces a general representation of audio, called "language of audio" (LOA)
  arXiv  Detail & Related papers  (2023-08-10T17:55:13Z)
• Self-Supervised Visual Acoustic Matching [63.492168778869726]
  Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
  arXiv  Detail & Related papers  (2023-07-27T17:59:59Z)
• MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training [74.32603591331718]
  We propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training.<n> Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attain state-of-the-art (SOTA) overall scores.
  arXiv  Detail & Related papers  (2023-05-31T18:27:43Z)
• An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization [58.88327181933151]
  In this paper, we propose an efficient query-based membership inference attack (MIA) Experimental results indicate that the proposed method can achieve competitive performance with only two queries on both discrete-time and continuous-time diffusion models. To the best of our knowledge, this work is the first to study the robustness of diffusion models to MIA in the text-to-speech task.
  arXiv  Detail & Related papers  (2023-05-26T16:38:48Z)
• Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data [24.608764078208953]
  Subgraphs are constructed by sampling the entire pool of available training data to exploit the relationship between labelled and unlabeled audio samples. We evaluate our model on three benchmark audio databases, and two tasks: acoustic event detection and speech emotion recognition. Our model is compact (240k parameters), and can produce generalized audio representations that are robust to different types of signal noise.
  arXiv  Detail & Related papers  (2022-01-31T21:32:22Z)
• COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations [32.456824945999465]
  We propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks.
  arXiv  Detail & Related papers  (2020-06-15T13:17:18Z)
• Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation [51.37980448183019]
  We propose Audio ALBERT, a lite version of the self-supervised speech representation model. We show that Audio ALBERT is capable of achieving competitive performance with those huge models in the downstream tasks. In probing experiments, we find that the latent representations encode richer information of both phoneme and speaker than that of the last layer.
  arXiv  Detail & Related papers  (2020-05-18T10:42:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.