Most existing detection algorithms of video frame copy-move forgery in time domain were designed for the copy-move forgery of video sequence containing 20 frames at least, and are difficult to detect single frame forgery. While according to the characteristics of human visual perception, 15 frames at least were needed to modify the video meaning. So when goal in vision was made by the tampering, continuous operation and many times were needed. In order to detect the tampering, a detection algorithm based on quantized Discrete Cosine Transform (DCT) coefficients for continuous and repeated single frame copy-move forgery in videos was proposed. Firstly, the video was converted into images, and quantized DCT coefficients were taken as the feature vector of a frame image. Then, the similarity between frames was measured by calculating Bhattacharyya coefficient, and threshold was set to judge the abnormal similarity between two adjacent frames. Finally, whether the video was tampered and the tampered positions were determined by the continuity of frames with abnormal similarity and the number of continuous frames. The experimental results show that the proposed algorithm can detect the video with different scenarios, it possesses fast detection speed, and is not affected by further compression factors, but also is of high accuracy and low omission ratio.