Faculty Publications
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Item Deepfake Audio Detection Using Quantum Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pandey, A.; Rudra, B.Artificial intelligence makes it easy for humans to create high-quality images, speech, audio dubbing, and more. However, this technology is often misused to create fake content, such as phony speech, which is then made public to tarnish someone's image. This technology is known as deepfake, which uses deep learning, a field of artificial intelligence, to generate fake content. Advancements in deepfake technology pose the challenge of detecting fake content. Although many classical models exist to detect fake content, they often do not consider suitable audio features, and training these classical models is resource-intensive. Therefore, in this paper, we use a recently created real-time AI-generated fake speech dataset and propose a method to detect fake content using quantum learning models. This emerging technology leverages the properties of quantum mechanics to increase processing speed. We have trained the quantum learning models using the Lightning Qubit simulator. © 2024 IEEE.Item Hybrid Classical Quantum Learning Model Framework for Detection of Deepfake Audio(Science and Technology Publications, Lda, 2025) Pandey, A.; Rudra, B.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). © 2025 by SCITEPRESS – Science and Technology Publications, Lda.