Comparison of Deepfakes Detection Techniques
This research presents a thorough comparative analysis of current state-of-the-art deepfake detection methods and identifies the factors that contribute to the performance degradation of deep fake detection models currently being used when tested against a comprehensive dataset.
Abstract
Detection of fake audio and video is a challenging problem. Deepfake is popularly used for creating fake audio and video content using deep learning. Deepfakes, artificially created audiovisual interpretations can be used to degrade the reputation of a renowned person, hate-speech, or affect public belief. The development of novel methods for identifying various deep fake video types has received a significant amount of research throughout the years. In this research, we present a thorough comparative analysis of current state-of-the-art deepfake detection methods. The primary goal of our research is to identify the factors that contribute to the performance degradation of deepfake detection models currently being used when tested against a comprehensive dataset.