Face authentication has been shown to be vulnerable against three main kinds of attacks: print, replay, and 3D mask. Among those, video replay attacks appear more challenging to be detected. There exist in the literature many countermeasures to face spoofing attacks, but a sophisticated detector is still needed to deal with particularly high-quality video based attacks. In this work, we perform analysis on the noise residual in frequency domain, and extract discriminative features by using a dynamic texture descriptor to characterize video based spoofing attacks. We propose a promising detector, which produces competitive results on the most challenging dataset of video based spoofing.