Quantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection

Name: Quantum Vision Theory Applied to Audio Classification for Deepfake Speech Detection
Uploaded: 2026-10-14T01:30:39.077142+00:00
Duration: 461 s
Description: The Quantum Vision (QV) theory transforms audio spectrograms into quantum-inspired information waves before classification. This method uses a QV block to create these wave features for training QV-CNN and QV-ViT models on deepfake speech detection. Experiments show these models significantly outperform standard CNN and ViT, achieving high accuracy on the ASVspoof dataset.

The Quantum Vision (QV) theory transforms audio spectrograms into quantum-inspired information waves before classification. This method uses a QV block to create these wave features for training QV-CNN and QV-ViT models on deepfake speech detection. Experiments show these models significantly outperform standard CNN and ViT, achieving high accuracy on the ASVspoof dataset.

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