IEEE Technology Policy and Ethics

July 2022

Quantum Machine Learning-based Detection of Fake News and Deep Fake Videos

By Saurabh Kamal, Engineering and Technology, Liverpool, John Moores University, United Kingdom, Vijay Kumar, Computer Science and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh, India, and Sahil Sharma, Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India

With the growth of multimedia technologies and Machine Learning (ML), it is becoming easier for individuals to create fake images/videos. Generative Adversarial Network (GAN) models are mainly used to generate accurate deepfakes, and then the fake content is distributed as news via the World Wide Web. Researchers are rapidly aiming to develop tools to combat the spread of false news, a major global threat. Fake content on major social media sites has had, and can have, real-world ramifications on people’s opinions and actions. This may only be the start of a race to identify solid algorithms that can combat deceitful information. The primary goal of this study is to identify fake news and deepfakes by leveraging quantum machine learning, and then comparing the training time with a traditional neural network model.

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