{"ID":2897613,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10447","arxiv_id":"2507.10447","title":"Evaluating Fake Music Detection Performance Under Audio Augmentations","abstract":"With the rapid advancement of generative audio models, distinguishing between human-composed and generated music is becoming increasingly challenging. As a response, models for detecting fake music have been proposed. In this work, we explore the robustness of such systems under audio augmentations. To evaluate model generalization, we constructed a dataset consisting of both real and synthetic music generated using several systems. We then apply a range of audio transformations and analyze how they affect classification accuracy. We test the performance of a recent state-of-the-art musical deepfake detection model in the presence of audio augmentations. The performance of the model decreases significantly even with the introduction of light augmentations.","short_abstract":"With the rapid advancement of generative audio models, distinguishing between human-composed and generated music is becoming increasingly challenging. As a response, models for detecting fake music have been proposed. In this work, we explore the robustness of such systems under audio augmentations. To evaluate model g...","url_abs":"https://arxiv.org/abs/2507.10447","url_pdf":"https://arxiv.org/pdf/2507.10447v1","authors":"[\"Tomasz Sroka\",\"Tomasz Wężowicz\",\"Dominik Sidorczuk\",\"Mateusz Modrzejewski\"]","published":"2025-07-07T16:15:02Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.AI\",\"cs.LG\",\"eess.AS\"]","methods":"[]","has_code":false}