T-2: DeepFake Generation and Detection: Part 1

Images, videos, and audios that are created or manipulated by AI algorithms, in particular, deep neural networks (DNNs), are a recent twist to the disconcerting problem of online disinformation. The AI-based fake contents, hereafter referred to as the DeepFakes, range from realistic images generated or edited with the generative adversarial network (GAN) models, to face-swapping videos created with auto-encoder network models (the origin of the namesake), and indistinguishable human voices created with recursive neural network models. The escalated concerns over the potential impacts of the DeepFakes have spawned rapid developments on the detection of DeepFakes in recent years, with promising performance reported on large-scale evaluation datasets. This tutorial will cover the fundamentals in the generation, detection, and other counter-technologies of DeepFakes and also provide the audience a comprehensive overview of the state-of-the-arts in these areas.