Deepfakes in digital media forensics: Generation, AI-based detection and challenges

Abstract

Deepfake technology presents significant challenges for digital media forensics. As deepfakes become increasingly sophisticated, the ability to detect and attribute manipulated media becomes more difficult. The main challenge lies in the realistic and convincing nature of deepfakes, which can deceive human perception and traditional forensic techniques. Furthermore, the widespread availability of open-source deepfake tools and increasing computational power contribute to the ease with which malicious actors can create and disseminate deepfakes. The challenges posed by deepfakes for digital media forensics are multifaceted. Therefore, the development of sophisticated detection algorithms, the creation of comprehensive datasets, and the establishment of legal frameworks are crucial in addressing these challenges. This paper provides a comprehensive analysis of current methods for deepfake generation and the issues surrounding their detection. It also explores the potential of modern AI-based detection techniques in combating the proliferation of deepfakes. This analysis aims to contribute to advancing deepfake detection by highlighting the limits of current detection techniques, the most relevant issues, the upcoming challenges, and suggesting future directions for research.