As security problem has become the tightest bottleneck in the application of face recognition systems, rendering a face recognition system robust against spoof attacks is of great significance to be dealt with. In this paper, aimed at video-based facial spoof attacks, an innovative face antispoofing algorithm based on local binary patterns (LBP) and multilayer discrete cosine transform (DCT) is proposed. First, we extract face images from a target video at a fixed time interval. Second, the low-level descriptors, i.e., the LBP features are generated for each extracted face image. After that, we perform multilayer DCT on the low-level descriptors to obtain the high-level descriptors (LBP-MDCT features). To be more exact, in each layer, the DCT operation is implemented along the ordinate axis of the obtained low-level descriptors, namely the time axis of the entire target video. In the last stage, the high-level descriptors are fed into a support vector machine (SVM) classifier to determine whether the target video is a spoof attack or a valid access. In contrast to existing approaches, the outstanding experimental results attained by the proposed approach on two widely-used datasets (Replay-Attack dataset and CASIA-FASD dataset) demonstrat its performance superiority as well as its low complexity and high efficiency.