Synopsis
The class will cover a wide range of perspectives on deepfakes, from their historical ancestors through their philosophical underpinnings and on to modern incarnations. Topics will be divided into two factions: Theoretical and Practical. The theoretical part will include readings of seminal works on simulative media as well as invited speakers that will discuss the societal impact of deepfakes on aspects of our lives: news, politics, entertainment and arts. The practical part will include programming tasks (Python) for hands-on usage of deepfake generators, a discussion of their powers as well as their limitations.
Topics covered:
- History, Philosophy of the Synthetic (“Simulative”) Media: Simulacra and Simulation, Hyperreality, Transhumanism, Faith in Fakes, Ultra-realistic CGI (from Photoshop to Unreal Engine v5 and The Matrix); Baudrillard, Bostrom, McLuhan, Eco
- Deepfake-and-X, Societal Impacts: Journalism, Politics, Activism, Social Media, Film Industry & Hollywood, Creative and artistic expression, Learning and motivation
- Deepfake engines: Machine and Statistical Learning basics, Generative AI models, GANs and other Decoders, hands-on deep generators.
- Deepfake detectors: Datasets, competitions and approaches.
Recommended literature
- Hands-On Machine Learning (2nd ed). Aurélien Géron. O’Reilly 2020
- Deep Learning with Python (2nd ed). Francois Chollet. Manning 2020
- Generative deep learning. David Foster. O’Reilly 2019.
- GANs in action. J. Langr and B. Vladimir. Manning 2019.
Recommended related courses
- Computer Visions, MIT Media Lab, MAS.S68 F’19, Shilkrot, Bernal and Maes
- Machine learning, by Andrew Ng, Coursera
- Deeplearning.ai, Coursera
- MIT EdX Machine Learning with Python (Barzilay & Jaakkola)
- Deepfakery, MIT Open Documentation & WITNESS