faceswap VS dfdc_deepfake_challenge

Head over to deepfakes/faceswap and install all the stuff that it asks you to do and then open the terminal with in faceswap env from anaconda.

But there's a nice survey on Arxiv here of various deepfake / face swap methods. Some of methods listed in the table on page 4, such as Faceswap and Faceswap-GAN, apparently use encoder-decoder models. I think Faceswap-GAN was the one that I was thinking of in particular; apparently it adds a perceptual loss and an adversarial loss to an autoencoder.

My current challenge is getting those masked wheel images to be able to swap between images, or to apply a new wheel on a car image. To get a decent result that doesn't look fake, it would have to do some minor warping and resizing. To me, this seems like exactly what the Deep Fakes repo does. https://github.com/deepfakes/faceswap

It keeps getting stuck at either"fatal: unable to access 'https://github.com/deepfakes/faceswap.git/':" or "Please run this script with Python version 3.7 or 3.8 64bit and try again."

https://github.com/deepfakes/faceswap bu uygulamadan yaparsın

Here's the faceswap github

Deepfaking as it is right now, is mostly two types of software, and two very different methods. The first is making a video with a "face swap". Those use software called Deepfacelab or Faceswap. I personally like Deepfacelab because it's more "configurable" at least how I see it. This is what you see on youtube or what makes headlines when the media decides to show clips of the dangerous political deepfakes.

For example I used a scanner using this model https://github.com/selimsef/dfdc_deepfake_challenge/blob/master/README.md

An example of this happening is this code from two years ago. When first created, it achieved 82.56% accuracy, but only achieved around 65% accuracy with newer videos. A new paper released last month managed to achieve 99% accuracy on modern deepfakes, but it's really only a matter of months until a new deepfake algorithm is created that diminishes this substantially either using it adversarially or through a novel technique.

Here's a deepfake detection github