  Altered and manipulated multimedia is increasingly present and widely
distributed via social media platforms. Advanced video manipulation tools
enable the generation of highly realistic-looking altered multimedia. While
many methods have been presented to detect manipulations, most of them fail
when evaluated with data outside of the datasets used in research environments.
In order to address this problem, the Deepfake Detection Challenge (DFDC)
provides a large dataset of videos containing realistic manipulations and an
evaluation system that ensures that methods work quickly and accurately, even
when faced with challenging data. In this paper, we introduce a method based on
convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that
extracts visual and temporal features from faces present in videos to
accurately detect manipulations. The method is evaluated with the DFDC dataset,
providing competitive results compared to other techniques.
