Deepfakes may be detected by paying attention attentively and learning how to avoid them.

Untrained eyes may have trouble spotting deepfake films due to their realism. It doesn’t matter whether these recordings are used for personal retribution, financial market manipulation or international relations destabilisation; they pose a serious challenge to the long-held belief that “seeing is believing.” That’s no longer the case.

If you show an algorithm multiple photographs of someone, it will utilise what it has seen to create new faces in the algorithm. The person’s voice is synthesised at the same moment, making it seem and sound as though they had uttered something fresh.

Earlier work by my research group enabled us to identify deepfake films that omitted the natural amount of blinking in people’s eyes, but the newest generation of deepfakes has adapted and our study has progressed.

As a result of our work, we can now tell whether a video has been manipulated simply paying attention to the pixels in certain frames. We went one step further and created a proactive safeguard to keep people safe from falling prey to deepfakes.

Recognising and addressing shortcomings

In two recent studies, we proposed methods for identifying deepfakes that have faults that the fakers can’t readily remedy. New facial expressions generated by a deepfake video synthesis algorithm don’t always match the precise head position, lighting circumstances, or camera distance of the original picture. Geometric transformations like as rotation, resizing, and other distortions are required to make the synthetic faces blend in with their environment. The final picture has digital artefacts as a consequence of this technique.

It’s possible that certain objects left behind following exceptionally traumatic transitions have caught your attention. These may give the appearance of a picture that has been doctored, such as blurring borders and too smooth skin. Even when individuals cannot notice the changes, more subtle modifications still leave traces, which we have trained an algorithm to identify. If you are blackmailed by التزييف العميق, you can contact us.

This alleged Mark Zuckerberg video has been identified as a hoax by an algorithm.

If the individual in the deepfake video isn’t staring straight at the camera, the artefacts will be different. There is no way for deepfake algorithms to construct faces in 3D based on video of actual people. An alternative is to create a flat picture of the face and then rotate, scale, and distort it to match the intended gaze direction.

They’re not particularly good at it yet, so there’s room for improvement. A nose-pointing algorithm was developed by us to determine which direction a person’s nose is pointing in a photo. It also determines the direction of the head’s movement based on the shape of the shaved head. When looking at a video of someone’s head, those lines should all fall into place. Deepfakes, on the other hand, often have them out of place. 

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Protecting oneself from deepfakes

There is an arms race going on when it comes to finding deepfakes; as fakers improve, so must our research to stay up and even gain a step ahead. Our technology would be more effective at identifying fakes if we could make the algorithms that produce deepfakes poorer. My team just figured out a technique to do exactly that.

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