In the era of advanced AI technologies, we are coming to a situation where we need to reconsider reality. Deepfake media has made a far greater impact on the public’s perception of information than face swaps, for example. There is a point of contact between these two technologies in some aspects, so see where the similarities are.
Deepfake technology is a revolutionary phenomenon that is on par with FB and YT. The ability to generate visual content and the power of manipulation makes us feel like little gods. Indeed, social networks and the Internet in general are flooded with deepfakes putting us in awe. Primarily intriguing, and only then fun, this digital editing technique caused quite a big stir. But first, in this blog post, we will see how much it can be connected with face swap technology and to what extent they touch in a technical sense.
The Deepfake Core Insight
This technology uses the greatest power of AI to create a hyper-realistic display along with the audio component. Relying on machine learning (ML) algorithms, at the very core of deepfake media are adversarial networks that are trained to recreate scenes from scratch, extracting data from training data. Here is a summary view:
- Data Collection: This is the very foundation of making deepfakes. As many videos and images as possible must be collected that will serve as a source of examples that the AI will serve as building material for the generated visuals.
- Training GANs(Generative Adversarial Networks): A GAN consists of two components: a generator and a discriminator. The generator creates fake images based on the learned data, while the discriminator checks the level of realism by comparing them with data sets. Both of these parts work together in a feedback loop. Why loop? This process is repeated again and again, forcing the generator to improve, until the discriminator can no longer distinguish the generated medium from the actual ones from the training data.
- Synthesis: Once trained, the generator becomes capable of fabricating synthetic displays that are a shockingly believable illusion of something that never happened.
Face Swaps: A Subset of Deepfake Technology
Before we dissect deepfake science, we’ll introduce you to how face swap works so you can see the difference. Face swap does not have the reach of deepfake technology, as it is only focused on facial changes in videos and images. The deepfake possibility can be used for face swaps and thus be considered a deepfake, but only a small part of deepfakes can be considered face swaps. Face swap technology cannot create new faces, but relies on the user’s input and target image or video.
Here’s what face swap looks like in technical terms
- Detection: For face swap to be pulled off the detection and recognition of the object as a face is essential. This operation includes both a source face image and a target video or photo. The algorithms create facial maps and go to the next step from there.
- Alignment: Once the faces are detected, AI algorithms do the alignment and warping on the target face to match the lines, contours, facial expressions, colors, and skin tone with the input face.
- Blending: The final step is fitting the input face after the target face is analyzed and removed, and blending to the point of natural look. The algorithms are performing the necessary adjustments along the way to appear a HQ output.
Technical Breakdown: Deepfakes vs. Face Swaps
Although face swap and deepfake technology share the same underlying method of operation, they are still very different visibly. Here’s what we mean:
Scope of Manipulation
- Deepfakes: The manipulation procedure includes a visual and audio component. This means that there are no restrictions on the generation of scripts, performances and even dubbing. In this way, an event that did not happen is obtained, and it gives extreme persuasiveness leaving no place to question the authenticity.
- Face Swaps: Focusing on one particular part of the picture, this technology replaces the face with another, while preserving the original movements and expressions.
Complexity
- Deepfakes: Much more complex is deep learning, which includes complete data, not just one piece of data
- Face Swaps: Far less complex composition, because it is exclusively face-oriented in operation and therefore does not require the same level of computational resources as deepfake AI.
Ethical Matters
- Deepfake: It represents a much greater threat than face swaps because due to the power of replicating reality down to the last pixel. It has unfortunately contributed a lot to the spread of misinformation, fake news, violation of privacy, and the development of many other negative practices.
- Face Swaps: It did cause some ethical dilemmas, specifically in the cases of creating and distributing non-consensual face-swap porn, it’s commonly perceived as harmful fun.
Use Cases for Deepfake Tech
- Film and TV Production
- Recasting Actors: This tech is very useful for digitally recasting the actors, seamlessly integrating new actors into the scenes and thus providing continuity.
- Historical Documentaries: Manipulation of the audio and video opens the possibility of reconstructing some historical event.
- Special Effects: Deepfake methods can be easily used for adding some special effects into feature films, both visual and audio.
- Ads and Marketing:
- Personalized Ads: Using deepfakes can be adjusted to make more engaging ads that can improve the brands’ rating
- Virtual influencers: Companies can generate digital influencers that will promote their products and engage with fans and consumers.
- Education and Training
- Training Simulations: Deepfake tech is very thankful for creating realistic training scenarios for various professional fields.
- Educational Material: Deepfakes can be used for engaging animated presentations or making video lessons for various subjects.
- Gaming
- Character Customization: Video game developers can employ deepfake technology to create NPCs (non-player characters) giving enhanced immersiveness.
Conclusion
Deepfake and face swap technologies are undoubtedly groundbreaking advancements in the field of digital creativity. As such, both cause excitement and new possibilities, but they also impose many negative questions and raise eyebrows. However, due to their different applications and powers, both of these editing methods have irreversibly changed the digital lives of us people, and major new advances are still ahead.
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