2020/09/16 by Shahroz Tariq, Tariq, Shahroz, Sang Yup Lee +4 · 16 citations
Computer Science · Mathematics · #Advanced Image Processing Techniques #Algorithm #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Convolutional neural network #Deep learning #Digital Media Forensic Detection #FOS: Computer and information sciences #Generalizability theory #Generative Adversarial Networks and Image Synthesis #I.4.9 #I.5.4 #Machine learning #Mathematics #Multimedia (cs.MM) #Pattern recognition (psychology) #Residual #Statistics #Transfer of learning #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.2009.07480
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/09/16 · openalex publication_date 2020/09/16 · arxiv updated 2020/09/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake videos with only a few victims or target images. This creates a significant social problem for everyone whose photos are publicly available on the Internet, especially on social media websites. Several deep learning-based detection methods have been developed to identify these deepfakes. However, these methods lack generalizability, because they perform well only for a specific type of deepfake method. Therefore, those methods are not transferable to detect other deepfake methods. Also, they do not take advantage of the temporal information of the video. In this paper, we addressed these limitations. We developed a Convolutional LSTM based Residual Network (CLRNet), which takes a sequence of consecutive images as an input from a video to learn the temporal information that helps in detecting unnatural looking artifacts that are present between frames of deepfake videos. We also propose a transfer learning-based approach to generalize different deepfake methods. Through rigorous experimentations using the FaceForensics++ dataset, we showed that our method outperforms five of the previously proposed state-of-the-art deepfake detection methods by better generalizing at detecting different deepfake methods using the same model.