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Ecole Centrale de Lyon

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Representative Papers

Improved Robustness in AI-Generated Music Detection

Jul 29, 2026

This work addresses the significant drop in robustness of existing AI-generated music detectors when confronted with simple audio transformations such as tempo changes and pitch shifts. To overcome this limitation, the authors propose a novel detection architecture that inherently incorporates frequency scaling invariance. The approach maps audio signals onto a logarithmic frequency axis via log-STFT and combines learnable cross-correlation filters with max-pooling to achieve translation invariance during inference. This is the first method to integrate frequency scaling invariance directly into the detection pipeline, simultaneously producing both a binary authenticity decision and an estimate of the applied tempo scaling factor, thereby enhancing model interpretability and adversarial robustness. Experimental results demonstrate that the proposed method maintains high detection accuracy under various audio transformation attacks, substantially outperforming current state-of-the-art techniques while accurately estimating transformation parameters.

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Latest Papers

Improved Robustness in AI-Generated Music Detection

Jul 29, 2026

This work addresses the significant drop in robustness of existing AI-generated music detectors when confronted with simple audio transformations such as tempo changes and pitch shifts. To overcome this limitation, the authors propose a novel detection architecture that inherently incorporates frequency scaling invariance. The approach maps audio signals onto a logarithmic frequency axis via log-STFT and combines learnable cross-correlation filters with max-pooling to achieve translation invariance during inference. This is the first method to integrate frequency scaling invariance directly into the detection pipeline, simultaneously producing both a binary authenticity decision and an estimate of the applied tempo scaling factor, thereby enhancing model interpretability and adversarial robustness. Experimental results demonstrate that the proposed method maintains high detection accuracy under various audio transformation attacks, substantially outperforming current state-of-the-art techniques while accurately estimating transformation parameters.

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