A survey on multimedia-enabled deepfake detection: state-of-the-art tools and techniques, emerging trends, current challenges & limitations, and future directions

Abstract

Rapid technological breakthroughs in recent years, like Deepfake, have made it feasible to produce synthetic media that is remarkably lifelike, but they also present significant hazards to public trust, privacy, and security. This survey paper reviews the latest techniques for detecting deepfakes, focussing on important components as image and video manipulation, audio spoofing, and multimodal synthesis. It features state-of-the-art methods including machine learning (ML), deep learning (DL), and multimodal architectures that are especially made to address the previously described deepfake criteria. The report provides a critical review of assessment measures used to assess detection model performance, including precision, accuracy, recall, computing effectiveness and efficiency, and fast responses to adversarial attacks. In order to assist direct future research, this highlights recent advancements in the subject, including explainable AI, federated learning, and self-supervised learning hierarchy. In order to examine the problems with adversarial attacks, scalability across different datasets, and the ethical implications of detection techniques, it is also vital to look into the technological and societal challenges surrounding multimedia-enabled deepfake detection. In particular, the usage of Blockchain Distributed Ledger Technology (BDLT) for traceability, lightweight modelling, and resilient systems forms for cross-model deepfake evaluation are discussed in this review study along with potential solutions to these limitations and areas for further research. This paper offers a comprehensive resource for future research, experts, and practitioners looking to combat the growing threat of deepfake, especially in the social media space, using innovative and useful detection tools.