We are Building an A.I. system that Learned to Lie to Us?
DeepFakes = DeepLearning + Fake

I have been hearing about concerns over deepfakes in recent years. Facebook is teaming up with Microsoft, the Partnership on A.I. coalition and academics from several universities to launch a contest (from late 2019 to spring of 2020) to better detect deepfakes. The social media giant spends $10 million on this contest.
What is DeepFakes?
The term deepfakes — a combination of the terms “deep learning” and “fake”, a form of artificial intelligence and originated around the end of 2017 from a Reddit user named “deepfakes”. He shared many videos involved celebrities’ faces swapped onto the bodies of actresses in pornographic videos while non-pornographic content included many videos with actor Nicolas Cage’s face swapped into various movies.

The danger of this technique is “the technology can be used to make people believe something is real when it is not. It can be used to weaken the reputation of a political candidate by making the candidate appear to say or do things that never actually occurred.
What does the Tech behind DeepFakes?
In 2016, Ian Goodfellow of Google Brain presented a tutorial entitled “Generative Adversarial Nets¹” to the delegates of the Neural Information Processing Systems (NIPS) conference in Barcelona. GANs are generative models; this technique has enabled computers to generate realistic data by using not one, but two, separate neural networks.
GANS help us to build an A.I. system that learned to lie to us.
GANs were not the first computer algorithm used to generate data, but their results and versatility set them apart from all the rest such as the ability to generate fake images with real like quality.

How GANS work?
It consists of two simultaneously trained models:
- The generator: trained to generate fake data.
- The discriminator: trained to discern the fake data from real examples.

GANs introduced a competition between a generator and a discriminator. They perform a min-max game together, where the generator's objective is to generate data which can fool the discriminator and the discriminators' objective is to accurately distinguish the generated data from real.
In the not so distant future, A.I. tools will help us to make all types of decisions and it also learned to lies to us. Don’t worry because we also can use A.I to detect manipulated contents.
References
[1] Ian Goodfellow, 2014, Google Scholar. Generative Adversarial Nets,
[2] Nick Bostrom, 2016, Oxford University Press. Superintelligence: Paths, Dangers, Strategies.
[3] The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, 2018. Malicious AI Report.

