Deepfake Detection Using Deep Learning: A Neural Network-Based Approach to Identifying AI- Generated Media

Deepfake Detection Using Deep Learning: A Neural Network-Based Approach to Identifying AI- Generated Media

Krishna Shrimali , Chirag Yadav

Department of Artificial Intelligence and Machine Learning, Universal college of Engineering Mumbai, India

Krishna.shrimali@universal.edu.in Chirag.yadav@universal.edu.in

Abstract— Over the last few decades, rapid progress in AI, machine learning, and deep learning has resulted in new techniques and various tools for manipulating multimedia. Though the technology has been mostly used in legitimate applications such as for entertainment and education, etc., malicious users have also exploited them for unlawful or nefarious purposes. For example, high-quality and realistic fake videos, images, or audios have been created to spread misinformation and propaganda, foment political discord and hate, or even harass and blackmail people. The manipulated, high-quality and realistic videos have become known recently as Deepfake. Various approaches have since been described in the literature to deal with the problems raised by Deepfake. Given the ease with which deep fake videos/images may be generated and shared, the lack of an effective deep fake detection system creates a serious problem for the world. However, there have been various attempts to address this issue, and deep learning-related solutions outperform traditional approaches.[5-6]

Keywords— Deepfake detection, video or image manipulation, deep learning , fake detection.