Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio

Atul Pandey, Bhawana Rudra

2025

Abstract

Artificial intelligence (AI) has simplified individual tasks compared to earlier times. However, it also enables the creation of fake images, audio, and videos that can be misused to tarnish the reputation of a person on social media. The rapid advancement of deepfake technology presents significant challenges in detecting such fabricated content. Therefore, in this paper, we particularly focus on the deepfake audio detection. Many Classical models exist to detect deepfake audio, but they often overlook critical audio features, and training these models can be computationally resource-intensive. To address this issue, we used a real-time AI-generated fake speech dataset, which includes all the necessary features required to train models and used Quantum Machine Learning (QML) techniques, which follow principles of quantum mechanics to process the data simultaneously. We propose a hybrid Classical-Quantum Learning Model that takes advantage of Classical and Quantum Machine Learning. The hybrid model is trained on a real-time AI-generated fake speech dataset, and we compare the performance with existing Classical and Quantum models in this area. Our results show that the hybrid Classical-Quantum model gives an accuracy of 98.81% than the Quantum Support vector Machine (QSVM) and Quantum Neural Network (QNN).

Paper Citation

in Harvard Style

Pandey A. and Rudra B. (2025). Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 231-239. DOI: 10.5220/0013258700003899

in Bibtex Style

@conference{icissp25,
author={Atul Pandey and Bhawana Rudra},
title={Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP},
year={2025},
pages={231-239},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013258700003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 2: ICISSP
TI - Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio
SN - 978-989-758-735-1
AU - Pandey A.
AU - Rudra B.
PY - 2025
SP - 231
EP - 239
DO - 10.5220/0013258700003899
PB - SciTePress