Deepfake Audio Detection Using Quantum Learning Models

Abstract

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.