With the rapid popularity of cameras on various devices, video chat has become one of the major ways for communication, such as online meetings. However, the recent progress of face reenactment techniques enables attackers to generate fake facial videos and use others’ identities. To protect video chats against fake facial videos, we propose a new defense system to significantly raise the bar for face reenactment-assisted attacks. Compared with existing works, our system has three major strengths. First, our system does not require extra hardware or intense computational resources. Second, it follows the normal video chat process and does not significantly degrade the user experience. Third, our system does not need to collect training data from attackers and new users, which means it can be quickly launched on new devices. We developed a prototype and conducted comprehensive evaluations. Experimental results show that our system can provide an average true acceptance rate of at least 92.5% for legitimate users and reject the attacker with mean accuracy of at least 94.4% for a single detection.