  The spread of misinformation through synthetically generated yet realistic
images and videos has become a significant problem, calling for robust
manipulation detection methods. Despite the predominant effort of detecting
face manipulation in still images, less attention has been paid to the
identification of tampered faces in videos by taking advantage of the temporal
information present in the stream. Recurrent convolutional models are a class
of deep learning models which have proven effective at exploiting the temporal
information from image streams across domains. We thereby distill the best
strategy for combining variations in these models along with domain specific
face preprocessing techniques through extensive experimentation to obtain
state-of-the-art performance on publicly available video-based facial
manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face
and FaceSwap tampered faces in video streams. Evaluation is performed on the
recently introduced FaceForensics++ dataset, improving the previous
state-of-the-art by up to 4.55% in accuracy.
