DeepFake-Notebooks

Record the foreign language audio.

Load the original video into the Wav2Lip notebook.

Process the video to sync lip movements to the new audio track.

Export the seamless dubbed version.

Scrape high-resolution source imagery of the actor from legacy films.

Train an SAEHD model on a lookalike actor's performance.

Iterate training until 0.02 loss is achieved.

Merge and composite using XSeg to handle hair and facial obstructions.

Swap the whistleblower's face with a synthetic, non-existent face.

Maintain the original eye movements and micro-expressions.

Render the output to ensure the original identity is unrecoverable.

Generate audio from an LLM response.

Use a pre-trained base model notebook to generate video from the audio.

Stream the resulting frames to the user interface.

Use a 'Src' dataset of the actor from 20 years ago.

Train a model to apply that skin texture to the current 'Dst' footage.

Blend the results with a 50% opacity merger to maintain modern performance.

Generate a diverse dataset of swaps using different notebook versions.

Label the metadata regarding which GAN was used.

Feed the synthetic data into a detection model to improve its accuracy.

Select a well-known public figure for the 'Src' data.

Record a performance as the 'Dst' actor.

Run the automated notebook pipeline to generate the parody video.