Summary

In this chapter, we studied deepfakes, which are synthetic media (images and videos) that are created using deep neural networks. These media often show people in positions that they have not been in and can be used for several nefarious purposes, including misinformation, fraud, and pornography. The impact can be catastrophic; deepfakes can cause political crises and wars, cause widespread panic among the public, facilitate identity theft, and cause defamation and loss of life. After understanding how deepfakes are created, we focused on detecting them. First, we used CNNs to detect deepfake images. Then, we developed a model that parsed deepfake videos into frames and used transfer learning to convert them into vectors, the sequence of which was used for fake or real classification.

Deepfakes are a growing challenge and have tremendous potential for cybercrime. There is a strong demand in the industry for professionals who understand deepfakes, their generation, the social...