Deepfake Detection using Spatiotemporal Convolutional Networks

• URL: http://arxiv.org/abs/2006.14749v1
• Date: Fri, 26 Jun 2020 01:32:31 GMT
• Title: Deepfake Detection using Spatiotemporal Convolutional Networks
• Authors: Oscar de Lima, Sean Franklin, Shreshtha Basu, Blake Karwoski, Annet George
• Abstract summary: Deepfake detection methods only use individual frames and therefore fail to learn from temporal information. We created a benchmark of performance using Celeb-DF dataset. Our methods outperformed state-of-theart frame-based detection methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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