  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.
