The facial expression reenacted forgery (FERF) is a very complicated and meticulous video tampering type compared to other video tampering types, such as the simple copy-paste of frames or objects. The best results of FERF can make the target actor’s facial expressions follow the changes of the source actor’s in real time. Existing video tampering detection methods aim at detecting simple tampering type, like intra-frame or inter-frame forgery, which function little on the detection of FERF. In this paper, a novel video forgery detection method is proposed to detect FERF. Through the attentive analysis of the general progress of FERF, some abnormal subtle changes in facial region is exposed and utilized to verify the authenticity of videos. Moment features of detailed wavelet transform coefficients and optical-flow features of the videos are combined as feature vectors put into Support Vector Machine (SVM) for the classification of original videos and forged ones. The experimental results show that the proposed method is effective on the detection of FERF. We also compare our results with previous popular copy-paste forgery detection algorithms.