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With the development of internet technology, artificial intelligence shows its rapid growth in recent year as well. More and more people pay attention to this field, including criminals inevitably. It is noted that one technology called “Deepfake” appeared on the internet at the end of 2017. As the name suggests, it is a portmanteau of “deep learning” and “fake”. In essence, Deepfake is a deep-learning framework in the field of image composite and replacement, which swap faces in images or videos. However detection of face forgery is still in its early stages, due to its novelty and complexity. In this paper we will demonstrate some dataset for deepfake forensics firstly, and then describe various existing detection methods. Those methods are reviewed in two parts: detection based on frame sequences and detection based on single frame. The former is implemented by differences between frames including human features, optical flow, timeline and so on, while the latter is based on features of single frame including extracted features and fusion boundary. Various convolutional Neural Networks (abbreviated as CNN) will be illustrated in this paper. Accordingly, performances of above algorithms are likely to be demonstrated and compared, and a further explanation will be given regarding on their applicable dataset. Finally, further research of face forgery detection of deepfake including methods and applications will be discussed.
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