Deepfake Detection using Spatiotemporal Convolutional Networks

Oscar de Lima; Sean Franklin; Shreshtha Basu; Blake Karwoski; Annet George

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arXiv · 2006.14749

Deepfake Detection using Spatiotemporal Convolutional Networks

Oscar de Lima·Sean Franklin·Shreshtha Basu·Blake Karwoski·Annet George

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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.

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BibTeX RIS

Oscar de Lima, Sean Franklin, Shreshtha Basu, Blake Karwoski, Annet George. 2020-06-26. Deepfake Detection using Spatiotemporal Convolutional Networks. https://arxiv.org/abs/2006.14749

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