A Comprehensive Review of Deepfake and its Detection Techniques
Tatwadarshi P. Nagarhalli, Ashwini M. Save, Sanket Patil, Uday Aswalekar · International Journal of Electrical and Electronics Engineering · 2024
Deepfake technology has emerged as a significant concern in the era of digital media, posing threats to various sectors by enabling the creation of highly realistic synthetic content. This paper presents a comprehensive review of deepfake techniques and detection methods. It analyzes 14 research papers covering a range of approaches, including machine learning algorithms, computer vision techniques, and signal processing methods. Key aspects explored include face and voice manipulation, multimodal fusion, and the use of attention mechanisms. The review highlights the challenges in detecting deepfakes, such as dataset bias and the arms race between creators and detectors. Additionally, it discusses the limitations of current detection techniques and the need for robust, scalable solutions. Through a critical analysis of the literature, this review provides insights into the strengths and weaknesses of existing approaches and identifies areas for future research. The paper contributes to understanding deepfake technology and its implications for society, emphasizing the importance of developing effective detection mechanisms to combat the spread of synthetic media.