Deepfake Detection using Spatiotemporal Convolutional Networks

Abstract

Better generative models and larger datasets have led to more realistic fake videos that can fool the human eye but produce temporal and spatial artifacts that deep learning approaches can detect. Most current Deepfake detection methods only use individual video frames and therefore fail to learn from temporal information. We created a benchmark of the performance of spatiotemporal convolutional methods using the Celeb-DF dataset. Our methods outperformed state-of-the-art frame-based detection methods. Code for our paper is publicly available at https://github.com/oidelima/Deepfake-Detection.

References in corpus (6)

• The Kinetics Human Action Video Dataset
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• RetinaFace: Single-stage Dense Face Localisation in the Wild
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• The Deepfake Detection Challenge (DFDC) Preview Dataset
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