使用多线性回归的Deepfake表示
Deepfake Representation with Multilinear Regression
摘要：
诸如GANs的生成神经网络架构可以用于生成合成实例以补偿真实数据的缺乏。然而，他们可能被用来创建可能导致社会，政治或经济动荡的媒体。一个新兴的媒体是“Deepfake”。可以区分这种媒体的技术是必不可少的。在本文中，我们提出了一种改进的多线性（张量）方法，线性和多线性回归的组合，用于表示假数据和真实数据。我们通过使用我们修改的多线性（张量）方法表示Deepfakes来测试我们的方法，并执行SVM分类，结果令人鼓舞。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
