What is Deepfake ????


Deepfake is technique used by any person where any person face image will be tempered or manipulated with high potential to deceive someone or to tarnished someone image.


Have you seen Barack Obama call Donald Trump a “complete dipshit”, or Mark Zuckerberg brag about having “total control of billions of people’s stolen data”, or witnessed Jon Snow’s moving apology for the dismal ending to Game of Thrones? Answer yes and you’ve seen a deepfake.




The 21st century’s answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake.

What are they for?


Many are used in pornographic.


Images of any celebrity SWAPED in Deepfake. 99% of those mapped faces from female celebrities on to porn stars. Deepfake technology is being weaponised against women.


Is it just about videos?

No. Deepfake technology can create convincing but entirely fictional photos from scratch. Audio can be deepfaked too, to create “voice skins” or ”voice clones” of public figures. There are many incident reported. Similar scams have reportedly used recorded WhatsApp voice messages.

How are they made?

University researchers and special effects studios have long pushed the boundaries of what’s possible with video and image manipulation. But deepfakes themselves were born in 2017 when a Reddit user of the same name posted doctored porn clips on the site. The videos swapped the faces of celebrities – Gal Gadot, Taylor Swift, Scarlett Johansson and others – on to porn performers.

It takes a few steps to make a face-swap video. First, you run thousands of face shots of the two people through an AI algorithm called an encoder. The encoder finds and learns similarities between the two faces, and reduces them to their shared common features, compressing the images in the process. A second AI algorithm called a decoder is then taught to recover the faces from the compressed images. Because the faces are different, you train one decoder to recover the first person’s face, and another decoder to recover the second person’s face. To perform the face swap, you simply feed encoded images into the “wrong” decoder. For example, a compressed image of person A’s face is fed into the decoder trained on person B. The decoder then reconstructs the face of person B with the expressions and orientation of face A. For a convincing video, this has to be done on every frame.

How do you spot a deepfake?

It gets harder as the technology improves. In 2018, US researchers discovered that deepfake faces don’t blink normally. No surprise there: the majority of images show people with their eyes open, so the algorithms never really learn about blinking. At first, it seemed like a silver bullet for the detection problem. But no sooner had the research been published, than deepfakes appeared with blinking. Such is the nature of the game: as soon as a weakness is revealed, it is fixed.

FACEBOOK AI

The Deepfake Detection Challenge Dataset is designed to measure progress on deepfake detection technology.

Facebook has launched its AI called FACEBOOK AI Its Deepfake Detection Challenge


Facebook’s artificial intelligence (AI) division put out this casting call so it could ethically produce deepfakes—a term that originally referred to videos that had been modified using a certain face-swapping technique but is now a catchall for manipulated video. The Facebook videos are part of a training data set that the company assembled for a global competition called the Deepfake Detection Challenge. In this competition—produced in cooperation with Amazon, Microsoft, the nonprofit Partnership on AI, and academics from eight universities—researchers around the world are vying to create automated tools that can spot fraudulent media.



Information collected from Facebook article page, This article appears in the January 2020 print issue as “Facebook Takes on Deepfakes.”

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CFE - Hanisha


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