Technological Threats: Deep Fake

 Photo manipulations were invented in the 19th century and quickly applied to cinema. Technology has improved and gained momentum with digital videos in the 20th century.

deep fake technology was created by scientists in the early 1990s and later by community network enthusiasts. Recently, methods have taken over the industry.

What is deep fake?

The title video is a cleverly made dip that Dr. in 2014. Published by Ian Goodfellow, a technology student who now works for Apple. Most Deepfac generators are based on Advanced Networks (GNAS).

In August 2018, researchers from the University of California at Berkeley launched a fake dance application and published an article that could possibly work as a dance expert using AI. This project extends the application of Dipfac throughout the body. The previous law was aimed at the head or part of the face.

Deepfake technology allows you to seamlessly access any video or photo in the world at any time without taking part. That power has existed for decades - and so the late actor Paul Walker came to life quickly and rapidly, but it took a year to become a wizard for its effects. Deepfac technology - the new automated computer graphics or machine learning system - can now quickly integrate images and videos.

Although the term "deep fake" is very confusing, researchers agree on their dislike of the term in terms of computer vision and graphics. It has become a memory that ranges from AI-inspired videos to images that seem like all possible scams.

How is deep fake:

A key component of deep fakes machine learning, which allows deep fake to be faster and cheaper. To create a deeply false video about a person, the creator first trained the neural network within a few hours of a person's original video to give it a true "understanding" of what it feels like to see it from many angles and in different light situations. Then you can say a unique copy of that person for trained computer graphics network training.

Although adding AI makes the process faster than before, the process takes time to create a verbal combination that brings a person into a fully imagined situation. To avoid complete scrolling of images and graphics, the manufacturer has to manually tweet many parameters of the trained program. The process is very simple.

Many assumed that the class of deep learning algorithms known as Generator Advertising Networks (GNAS) would be the main driver in the development of deep fake. The people produced by GAN cannot be separated from the real people. A preliminary review of the deep fake landscape, GANS has dedicated an entire section, indicating that it will allow anyone to create detailed fax.

However, the focus of this particular strategy was wrong, says Sue Liu of San Buffalo. “Most of the videos with deep fake nowadays are made using algorithms that don’t really think about GAN,” he said.

Janes work hard and need a lot of training data. Creating images takes more time than other techniques. GAN models are particularly suitable for image synthesis but not for video creation. It is very difficult for them to maintain a temporary continuity or to align the same image with another image.

Even the most famous “deep fake” term does not use GNS. Speaking to Rogan, the host of the Canadian intelligence agency Desha (now owned by Square), he never said that GNS was not involved. In fact, the lion's share of today's defects was created using various AI and non-AI algorithms.