SCARS™ Insight: DeepFake And The Frightening New World Online

Most People Have No Idea What Is Coming!

Much Less How This Works – It Is Almost Magic!

But the truth is that it is science, and while it is not understandable by those that lack a STEM education, it can be explained.

According to LyrnAI:

The process is based upon what is called “Generative Adversarial Networks” (GAN) which is a relatively new concept in Machine Learning, introduced for the first time in 2014.

The goal of GAN is to synthesize artificial samples, such as images, that are indistinguishable from authentic images.

A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity faces.

While GAN images became more realistic over time, one of their main challenges is controlling their output, i.e. changing specific features such pose, face shape and hair style in an image of a face.

However, these limitations, like all weaponized technology will become easier, faster, and more precise as time goes by. Soon we will never know who is on the screen. Will it be real, a synthesized face from a real person, or an artificial powered by an AI.

This technology will completely change the face of everything from Politics, to the Evening News, to Personal Communications, to Impersonation Scams. The gains made against transnational scamming will be lost because of this. Worst still is that scamming will move from being a person (human) to person crime to one that can all be done inside a server powered by AI.

This just further addresses the futility of trying to post photos of scammers (exposing them), since in the near future each one will be uniquely synthesized from different people.

Here Is A Very Detailed Explanation Of How All Of This Works:

[contentcards url=”https://www.lyrn.ai/2018/12/26/a-style-based-generator-architecture-for-generative-adversarial-networks/” target=”_blank”]

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SCARS™ Team
A SCARS Division
Miami Florida U.S.A.

TAGS: SCARS, Future Of Scams, DeepFake, GAN, Generative Adversarial Networks, Machine Learning, Fake Faces,

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