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World Congress 2021 • Jun 28, 2021

Deepfakes in Realtime - How Neural Networks Are Changing Our World

Thomas Endres , Martin Förtsch , Jonas Mayer

How do you build real-time deepfakes that rival Hollywood CGI? Discover how autoencoders and GANs conquer live video bottlenecks to instantly swap faces with anatomical accuracy.

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#1 about 5 min

Introducing deepfakes and the controversy of manipulated media

An overview of public awareness and potential deception surrounding manipulated video content.

#2 about 4 min

Differentiating traditional video fakes from neural network models

How methods like audio-based lip sync, face swap, and parametric face models lack foundational deep learning processing.

#3 about 4 min

Defining true deepfakes generated by trained neural networks

Authentic deepfake generation requires deep learning models like DeepFaceLab to synthesize highly realistic facial replacements.

#4 about 4 min

Preparing video datasets and extracting faces with FaceNet

Extracting and aligning facial landmarks from source videos ensures accurate feature mapping for neural model consumption.

#5 about 5 min

Training autoencoders for latent space facial feature reconstruction

Shared encoders and separate decoders compress and reconstruct facial expressions by leveraging a combined latent space representation.

#6 about 3 min

Implementing the deepfake inference workflow for face generation

The underlying workflow merges the generated facial mask with target video frames using fully trained autoencoder components.

#7 about 5 min

Achieving real-time tracking performance with OpenCV and segmentation

Replacing slow tracking heuristics with OpenCV processes and transfer learning models like MobileNet and U-Net yields rapid full-head segmentation.

#8 about 3 min

Enhancing realistic mask blending through OpenCV image inpainting

Integrating OpenCV image inpainting routines cleanly removes the original head before seamlessly applying the synthetic facial mask.

#9 about 10 min

Stabilizing multiple face generative models using GAN training

Generative adversarial networks counter model deterioration by intelligently adapting the loss balance between a generator and discriminator.

#10 about 3 min

Demonstrating real-time streaming results and target generation constraints

Live showcase examples of head-swapping multiple identities emphasize operational constraints and persistent source limitations.

#11 about 4 min

Exploring media entertainment applications and countering synthetic fakes

Analyzing professional commercial opportunities to replace expensive CGI alongside reviewing an ongoing digital arms race in threat detection.

#12 about 3 min

Future technological advancements in voice cloning and synchronization

Current constraints in architectural pipelines limit deploying sophisticated real-time voice synthesis and accurate full-body physical deepfakes.

Matching moments

5:25 min

Technical pipeline for real-time video deepfakes

Martin Förtsch Martin Förtsch +1 · WWC 2024

2:47 min

Exploring computer-generated content and modern deepfake capabilities

George Proorocu George Proorocu +1 · WWC 2024

1:10 min

Analyzing visual artifacts in synthetic media

Martin Förtsch Martin Förtsch +1 · WWC 2024

2:52 min

Audience questions on deepfake detection, regulations, and ethics

Cheuk Ho · WWC 2023

1:22 min

Passing technical interviews using active video and audio deepfakes

George Proorocu George Proorocu · WWC Europe 2026

2:17 min

Exploiting deepfakes in live video conferencing

Martin Förtsch Martin Förtsch +1 · WWC 2024

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