The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks. The number of countermeasures is gradually increasing and fairly good results have been reported on the publicly available databases. There exists no superior anti-spoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, it is important to find out complementary countermeasures and study how they should be combined in order to construct an easily extensible anti-spoofing framework. In this paper, we address this issue by studying fusion of motion and texture based countermeasures under several types of scenic face attacks. We provide an intuitive way to explore the fusion potential of different visual cues and show that the performance of the individual methods can be vastly improved by performing fusion at score level. The Half-Total Error Rate (HTER) of the best individual countermeasure was decreased from 11.2% to 5.1% on the Replay Attack Database. More importantly, we question the idea of using complex classification schemes in individual countermeasures, since nearly same fusion performance is obtained by replacing them with a simple linear one. In this manner, the computational efficiency and also probably the generalization ability of the resulting anti-spoofing framework are increased.