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Deepfakes Detection with Automatic Face Weighting
Open accessAbstract
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.
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“The recent availability of large deepfake video databases allowed researchers to train and test detection approaches based on very deep neural networks, such as Xception [1], capsules networks [8], EfficientNet [2], and discriminators of GANs used to generate synthetic images [9], which were shown to outperform the methods based on shallow CNNs, facial physical characteristics [10], [11], [12], [1”
From Improving Generalization of Deepfake Detection With Data Farming and Few-Shot Learning (2022) · cites this paper as [2]
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Metadata
- Authors
- Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., & Delp, E. J.
- Affiliations
- Purdue University West Lafayette (US)
- ORCID
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 2851-2859
- OpenAlex
- W3020392129
- Cited by
- 181
- References
- 41
- Citations by year
- 2026: 18 · 2025: 23 · 2024: 39 · 2023: 37 · 2022: 42 · 2021: 13 · 2020: 9
- FWCI
- 7.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- deepfake detection, video manipulation, DFDC, convolutional neural networks, recurrent neural networks, temporal features, visual features, social media misinformation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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