Deepfake Representation with Multilinear Regression
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Abstract (arXiv)
Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data. However, they may be employed to create media that may cause social, political or economical upheaval. One emerging media is "Deepfake".Techniques that can discriminate between such media is indispensable. In this paper, we propose a modified multilinear (tensor) method, a combination of linear and multilinear regressions for representing fake and real data. We test our approach by representing Deepfakes with our modified multilinear (tensor) approach and perform SVM classification with encouraging results.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 15 Aug 2021.
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14 of 32 references.
References · 14
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