Deepfakes are the synthesized digital media in order to create ultra-realistic fake videos to trick the spectator. Deep generative algorithms, such as, Generative Adversarial Networks(GAN) are widely used to accomplish such tasks. This approach synthesizes pseudo-realistic contents that are very difficult to distinguish by traditional detection methods. In most cases, Convolutional Neural Network(CNN) based discriminators are being used for detecting such synthesized media. However, it emphasise primarily on the spatial attributes of individual video frames, thereby fail to learn the temporal information from their inter-frame relations. In this paper, we leveraged an optical flow based feature extraction approach to extract the temporal features, which are then fed to a hybrid model for classification. This hybrid model is based on the combination of CNN and recurrent neural network (RNN) architectures. The hybrid model provides effective performance on open source data-sets such as, DFDC, FF++ and Celeb-DF. This proposed method shows an accuracy of 66.26%, 91.21% and 79.49% in DFDC, FF++, and Celeb-DF respectively with a very reduced No of sample size of approx 100 samples(frames). This promises early detection of fake contents compared to existing modalities.

本文主要研究深度伪造影像技术中因时域信息缺失导致传统方法检测失误的问题，并基于光流特征提取、CNN和RNN混合模型开展研究，实现对DFDC、FF++和Celeb-DF等开源数据集中深度伪造影像的有效检测，样本数量不足100帧时在DFDC、FF++、Celeb-DF数据集上的精度为66.26%、91.21%和79.49%，有望实现对深度伪造内容的早期检测。

基于光流特征的混合卷积神经网络 - 长短期记忆网络在视频深度伪造检测中的应用