Development of a Multimodal Framework for Deepfake Detection: Combining Visual and Audio Analysis
Ahmed Ashraf Bekheet, Ghada Ahmed Khoriba, Amr Sabry · Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Machine learning and social media advancements enable the rapid spread of realistic fake content, encompassing images, videos, and audio.Initially, fake content generation primarily focused on manipulating either audio or video streams.However, recent advancements in deep learning have enabled more sophisticated alterations, commonly called "deepfakes."While existing research predominantly concentrates on detecting fake videos by exploiting either visual or audio modalities, few approaches address audio-visual deep-fake detection.Nevertheless, these methods often need more accuracy when evaluated on a multimodal dataset with deepfake videos and manipulations in both streams.Due to neglecting facial features in preprocessing and using traditional training models.In response to this challenge, we propose a robust audio-visual deepfake detection (MAVDD) approach that analyzes audio and visual streams to enhance detection capabilities.Effectively utilizing pretrained models in image classification tasks for detecting visual deepfakes, alongside advanced preprocessing techniques for optimal facial and audio features extraction.Our experiments conducted on the multimodal audio-visual deepfake dataset "FakeAVCeleb" demonstrate that our proposed approach surpasses both unimodal (audio-only and visual-only) and multimodal (audio-visual) deepfake detection approaches in terms of accuracy and AUC (Area Under The Curve) as dedicated to tables I, II, and III.The implementation of our research work and the dataset are publicly available at the following link: https://github.com/mutlimodalDeepfakeDetection/AV-detector.