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Course 24 - Machine Learning for Red Team Hackers | Episode 5: The Complete Guide to Deepfake Creation
In this lesson, you’ll learn about:
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
- What deepfakes are and how neural networks enable face, voice, and style transfer
- The standard face swap pipeline: extraction → preprocessing → training → prediction
- Why conducting a local dry run helps validate datasets before scaling to expensive GPU environments
- The importance of face alignment, sorting, and dataset cleaning to reduce false positives
- How lightweight models are used for parameter tuning before full-scale training
- The role of GPU acceleration in deep learning workflows
- Why cloud platforms like Google Cloud are used for large-scale model training
- The importance of compatible drivers (e.g., NVIDIA drivers) in deep learning setups
- How frameworks such as TensorFlow power neural network training
- How frame rendering and encoding tools like FFmpeg compile processed frames into video
- How training previews help visualize model convergence from noise to structured outputs
- Always obtain explicit consent from anyone whose likeness is used
- Understand laws regarding impersonation, fraud, and non-consensual synthetic media
- Consider watermarking or disclosure when creating synthetic content
- Be aware that deepfake techniques are actively studied in media forensics and detection research
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
View all episodes
By CyberCode AcademyIn this lesson, you’ll learn about:
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy
- What deepfakes are and how neural networks enable face, voice, and style transfer
- The standard face swap pipeline: extraction → preprocessing → training → prediction
- Why conducting a local dry run helps validate datasets before scaling to expensive GPU environments
- The importance of face alignment, sorting, and dataset cleaning to reduce false positives
- How lightweight models are used for parameter tuning before full-scale training
- The role of GPU acceleration in deep learning workflows
- Why cloud platforms like Google Cloud are used for large-scale model training
- The importance of compatible drivers (e.g., NVIDIA drivers) in deep learning setups
- How frameworks such as TensorFlow power neural network training
- How frame rendering and encoding tools like FFmpeg compile processed frames into video
- How training previews help visualize model convergence from noise to structured outputs
- Always obtain explicit consent from anyone whose likeness is used
- Understand laws regarding impersonation, fraud, and non-consensual synthetic media
- Consider watermarking or disclosure when creating synthetic content
- Be aware that deepfake techniques are actively studied in media forensics and detection research
You can listen and download our episodes for free on more than 10 different platforms:
https://linktr.ee/cybercode_academy