AI fake porn could cast any of us
In the case of revenge porn, people often ask: If the photos weren’t taken in the first place, how could ex-partners, or hackers who steal nude photos, post them?
Unfortunately, there’s now an answer to that rhetorical question. Forget fake news. We are now in the age of fake porn.
Fake, as in, famous people’s faces – or, for that matter, anybody’s face – near-seamlessly stitched onto porn videos. As Motherboard reports, you can now find actress Jessica Alba’s face on porn performer Melanie Rios’ body, actress Daisy Ridley’s face on another porn performer’s body and Emma Watson’s face on an actress’s nude body, all on Celeb Jihad – a celebrity porn site that regularly posts celebrity nudes, including stolen/hacked ones.
Here’s Celeb Jihad crowing about a clip of a woman showering:
The never-before-seen video above is from my private collection, and appears to feature Emma Watson fully nude…
The word “appears” is key. It is, rather, an example what’s being called a deepfake.
Motherboard came across the “hobby” of swapping celebrities’ faces onto porn stars’ bodies in December, when it discovered a redditor named “deepfakes” who had made multiple convincing porn videos, including one of “Wonder Woman” star Gal Gadot apparently having sex with her stepbrother.
He also created porn videos with publicly available video footage of Maisie Williams, Scarlett Johansson, and Taylor Swift, among others. Deepfakes posted the hardcore porn videos to Reddit.
https://nakedsecurity.sophos.com/2018/01/26/ai-fake-porn-could-cast-any-of-us/
Well the fake nudes weiners are gonna love this.
Well, young Brown KISSED a girl ... when he was in his twenties!!! What a MONSTER! ... a PERVERT!! ... He should be put on a list!!! He should be run out of town!!! Chemical sterilization is called for!
yeah of the whole concept of democracy.
[youtube]ohmajJTcpNk[/youtube]
Face2Face: Real-time Face Capture and Reenactment of RGB Videos (CVPR 2016 Oral)
Paper Abstract
We present a novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion.
To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling.
At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target.
The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination.
We demonstrate our method in a live setup, where Youtube videos are reenacted in real time.
