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Generative neural network architectures such as GANs, may be used to generatesynthetic instances to compensate for the lack of real data. However, they maybe employed to create media that may cause social, political or economicalupheaval. One emerging media is "Deepfake".Techniques that can discriminatebetween such media is indispensable. In this paper, we propose a modifiedmultilinear (tensor) method, a combination of linear and multilinearregressions for representing fake and real data. We test our approach byrepresenting Deepfakes with our modified multilinear (tensor) approach andperform SVM classification with encouraging results.

Deepfake Representation with Multilinear Regression