Publication

Abstract : Deepfake technology enables the creation of highly realistic but fabricated videos, raising significant concerns about the authenticity and reliability of digital media. As deepfakes become increasingly sophisticated, effective detection methods have become crucial. One promising approach involves Remote PhotoPlethysmography (rPPG), a non-contact method that measures subtle changes in skin color to estimate heart rate. rPPG is a powerful tool for detection as deepfake videos often fail to replicate the physiological signals inherent in real human subjects. This survey aims to provide a comprehensive understanding on various rPPG-based techniques for identifying deepfakes and analyzing their effectiveness, challenges, and future potential. To improve the detection accuracy, methods that enhance rPPG signals in combination with machine learning models are examined. Real-world applications, including the detection of fake celebrity videos that are employed in propaganda, illustrate the applicability of these techniques. Through a comprehensive review of existing rPPG-based deepfake detection techniques, this survey aims to inform future research and development endeavors in the area, thereby contributing to broader efforts to secure digital content from tampering and exploitation.