Thomas Endres, Martin Förtsch & Jonas Mayer
Deepfakes in Realtime - How Neural Networks Are Changing Our World
#1about 4 minutes
The danger and deception of deepfake technology
An introductory example using a fake Obama video highlights the potential for misinformation and the importance of public awareness.
#2about 4 minutes
Differentiating fakes from true deepfakes
A breakdown of related but distinct technologies like lip syncing, face swapping, and real-time reenactment clarifies what constitutes a genuine deepfake.
#3about 13 minutes
How traditional deepfakes are made with Deep Face Lab
The original deepfake workflow involves preparing a dataset with FaceNet, training a shared autoencoder, and then running inference to generate the final video.
#4about 10 minutes
Building a real-time deepfake pipeline
To achieve real-time performance, the original pipeline is modified with faster face segmentation using MobileNet and U-Net, and image inpainting to remove the original head.
#5about 8 minutes
Using GANs to improve deepfake image quality
Generative Adversarial Networks (GANs) are introduced to the training process, where a generator and a discriminator compete to produce more realistic and artifact-free faces.
#6about 2 minutes
Live demo and limitations of data-driven models
A live demonstration shows the real-time head replacement and reveals a key limitation where the model can only reproduce expressions seen in its training data.
#7about 4 minutes
Applications in entertainment and detecting fake news
Deepfakes offer a cost-effective alternative to CGI in the movie industry and can also be used to train discriminator models for detecting fake news.
#8about 2 minutes
The future of deepfakes beyond face swapping
The next evolution of deepfake technology will likely involve higher resolutions, full-body manipulation, and real-time voice cloning to create more comprehensive fakes.
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