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The State of Deepfakes: Reality Under Attack. A 2018 Report [pdf]

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Re: The State of Deepfakes: Reality Under Attack. A 2018 Report [pdf]

#4
> GAN: Generative Adversarial Networks, a specific kind of deep learning algorithm that can train a neural network to generate realistic imagery. Whilst GANs are not integral to creating synthetic media, they represent the most significant development in the how new kinds of synthetic media are created.

In the future, our enemies will be Anonymous GANs, or GANNONs

Re: The State of Deepfakes: Reality Under Attack. A 2018 Report [pdf]

#7
post #5

How does one create a deepfake?

If you know some basics deep learning, you can find source code on github to play with. You will also find tutorials made by communities of developers and users.

Otherwise, if you look deep enough on the Internet, there are already websites offering deepfake as-a-service for a rather low price.

Not all these tools were developed for malicious purposes. Quite the opposite. But today it is up to everyone of us how this tech get utilised. And while it's becoming easier and cheaper to get realistic synthetic imagery, with better tools day by day, it is not yet obvious how we will cope with fake videos in our society.

Re: The State of Deepfakes: Reality Under Attack. A 2018 Report [pdf]

#8
post #6

Will deepfake detection be built into antivirus software?

That's a good question, maybe!

The way we see it: if today we are used to scan files downloaded from potentially unsafe sources, tomorrow we will get used to scan videos to check their authenticity -- check that they have not been tampered with, changing their conveyed meaning. Arguably, the same will hold for social media and video hosting websites.

While traditional malware are there to infect other piece of software, fake videos targets the human brain. This is more akin to social engineering.

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