Have You Ever Thought How Videos Are Edited in Deepfakes?

This innovative AI technology creates so much more. With a single selfie, you can be anyone and anywhere.

Table of Contents

What Is Deepfake?

The traditional Chinese face-changing skill is done through masks, while the face-changing in Deepfakes is realized by AI technology. It replaces one person’s facial features with a video made by another person. There is no sense of disobedience, which is eye-opening. Just like pictures, videos can be “PS” too. From the perspective of the development trend of technology, Deepfake will definitely get a boost.

How Deepfakes Are Created?

Machine learning is the main means of making a deepfake, which expedites the process and cut the cost. Deepfake is a combination of multiple technologies, and it’s based on an autoencoder. Autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. The encoding is validated and refined by attempting to regenerate the input from the encoding. To generate a deepfake video, developers need to build a computational model by training on hours of footage of the target to obtain a more natural merging effect.

How to Make a Deepfake Without Coding?

There are apps like Zao, Reface, Facemagic, and online websites like Deepswap, and Deepfakesweb that can help you make a deepfake video. Let’s take Deepswap as an example and check the steps to make a nice face swap.

Deepfake Step 1:

Deepswap deepfakes tutorial step 1 — Deepswap tutorial step 1

Open the site HERE

Upload the original video/photo/GIF/meme you want to swap with.

Recommended Size & Length:

Video: Max 100 MB, 120 s

Photo: Max 10 MB

GIF: Max 15 MB

Deepfake Step 2:

Add A Face you want to swap which can be a selfie or a portrait of someone.

Deepfake Step 3:

Press the Create bottom.

Deepfake Step 4:

Save the Deepfake or Upload the next file.

Let Deepfake Go Further

For the progress of AI research, the scientific community must be able to collaborate with cutting-edge models to effectively explore their promise while looking for any loopholes. Collaboration across research organizations is critical to the responsible development of deepfake AI systems and deepfake detection mechanisms.