A deepfake is created by a computer program that can teach itself how to recreate a face. By adjusting parameters in its system, the program becomes better in recreating a specific person’s face, this is a type of deep learning. The programme then overlays the face it has recreated onto an existing video – like a digital mask. You can see traces of such a mask in this video.

This experiment was done with the same source video exported at two different frame rates – both models were trained with the exact same studio setup. The amount of training cycles per image is equal, but the training time was longer with the bigger dataset. You can clearly see that the algorithm trained with more images can produce a more refined result that better matches the target.

The deepfake was exported with a resolution of 64 px. The lower resolution means it took less time to train the algorithm, because the model only had to learn how to create a low-resolution image. In close up face shots, the low resolution is evident.

Training time is related to the number of times the algorithm processes the images. The process involves creating a face (or digital mask), comparing it with the source image, and then making adjustments to improve the likeness of the mask to the source. The model goes through this cycle once for all source images, and then starts again. The time it takes depends on the power of the computer used.

Facial reenactment takes much more computing power but is much harder to recognise. Many of the challenges posed by source videos do not apply to reenactment, but the algorithm acts in a similar way. The recreated parts of the face will be slightly blurred and less detailed.

Also, pay attention to the audio and look for flaws or lip-sync problems. Using the insight you’ve learned on this website, question whether a video is likely to be a target and if the conditions are suitable for a possible deepfake. If in doubt, always check the source of the video.