Deepfake Generation and Detection: Case Study and Challenges

Abstract

In smart communities, social media has allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create fake images/audio/and video streams of a person or use some person's audio and visual details to fit other environments. Thus, deepfakes are specifically used to disseminate fake information and propaganda on social circles that tarnish the reputation of an individual or an organization. Recently, many surveys have focused on generating and detecting deepfake images, audio, and video streams. Existing surveys are mostly aligned toward detecting deepfake contents, but the generation process is not suitably discussed. To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. We discuss a comparative analysis of deepfake models and public datasets present for deepfake detection purposes. We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes.