  The DeepFakes, which are the facial manipulation techniques, is the emerging
threat to digital society. Various DeepFake detection methods and datasets are
proposed for detecting such data, especially for face-swapping. However, recent
researches less consider facial animation, which is also important in the
DeepFake attack side. It tries to animate a face image with actions provided by
a driving video, which also leads to a concern about the security of recent
payment systems that reply on liveness detection to authenticate real users via
recognising a sequence of user facial actions. However, our experiments show
that the existed datasets are not sufficient to develop reliable detection
methods. While the current liveness detector cannot defend such videos as the
attack. As a response, we propose a new human face animation dataset, called
DeepFake MNIST+, generated by a SOTA image animation generator. It includes
10,000 facial animation videos in ten different actions, which can spoof the
recent liveness detectors. A baseline detection method and a comprehensive
analysis of the method is also included in this paper. In addition, we analyze
the proposed dataset's properties and reveal the difficulty and importance of
detecting animation datasets under different types of motion and compression
quality.
