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Generative AI with Python and TensorFlow 2

Modes of Operation

Learn how to generate authentic deepfake content by understanding the roles of source, target, and generated data. Discover the modes of operation such as replacement, swap, reenactment, and editing using GANs. Explore how these techniques enhance realism by reenacting gaze, mouth, expression, and pose, while considering ethical concerns associated with AI-driven face manipulation.

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Generating believable fake content requires taking care of multiple aspects to ensure that the results are as authentic as possible. A typical deepfake setup requires a source, a target, and the generated content.

  • The source, denoted with subscript ss, is the driver identity that controls the required output.

  • The target, denoted with subscript tt, is the identity being faked.

  • The generated content, denoted with subscript gg, is the result following the transformation of the source to the target.

Now that we have some basic terminology, let’s explore the different ways to generate deepfake content.

Disclaimer: This demonstration uses deepfake technology to show how images and videos can be manipulated using AI. It is intended purely for educational purposes to explore the capabilities of the technology, not to deceive or mislead. Ethical considerations are crucial when using this technology, and unauthorized use without consent is not supported.

Replacement

This is the most widely used form of generating fake content. The aim is to replace the specific content of the target (xtx_t) with that from the source (xsx_s ...