Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions

Abstract

Recent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extremely realistic and challenging to identify from authentic content. Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud. This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as associated challenges and future research trends. By synthesizing existing knowledge and research, this survey aims to facilitate further advancements in deepfake detection and mitigation strategies, ultimately fostering a safer and more trustworthy digital environment.