Liveness detection is an important countermeasure against spoofing attacks on biometric authentication systems. In the context of audiovisual biometrics, synchrony detection is a proposed method for liveness confirmation. This paper presents a novel, text-dependent scheme for checking audiovisual synchronization in a video sequence. We present custom visual features learned using a unique deep learning framework and show that they outperform other commonly used visual features. We tested our system on two testing sets representing realistic spoofing attack approaches. On our mobile dataset of short video clips of people talking, we obtained equal error rates of 0:8% and 2:7% for liveness detection of photos and video attacks, respectively.