Deepfake term is a combination of deep learning and fake. In the simplest terms, it means creating fake content by using deep learning. Let's clear something up before starting. Deepfake is quite different than nowadays's hot trend generative ai. Generative ai creates new and original content by using machine learning and ai, but deepfake changes some parts of current content, and creates a synthetic one.

Deepfake is mostly used for face manipulation. The focus of these studies is swapping a person's face with another person's. The critical point is making the final result look natural. In successful content, the viewer doesn't detect that the face is not original. And lately, deepfake technology creates more and more successful results. Conceptual, this success can be achieved not only in images but also in video content too.

What kind of technology does Deepfake use?

‍The human eye can easily detect unsuccessful deepfake content. But successful content is hard to detect. To create a successful result content creators consider some elements.

The first of these elements is selecting similar faces. The presentive face and final face should have similar expressions, angles, and sizes. And another point is transitions. Transitions should be soft and smooth to make a more natural-looking image. A sharp transition between the head and background is an important tip to detect synthetic content. Transitions between face and body, head and body harmony, shadows, and lights on the face are crucial details too.

But why the human eye can not detect fake content? Why does ai do it better? Human perception is based primarily on connections and is weak on decision-making based on technical details. Let's explain it with an example. All cameras have a track named noise. This track is a result of the camera sensors' being imperfect. And they are unique for each camera sensor and work like a fingerprint for the sensor. If the presented face image and original face image were taken by different cameras, they will different noises. But the human mind can not detect it. It focuses on the consistency of the final image. So our most effective tool to detect this fake content is artificial intelligence.

Many different ways deepfake technology use. They all have advantages and disadvantages. But it is safe to say that whatever way they use they create more and more successful results. And these successful results become a great threat to society.

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What kind of threat does Deepfake create?

‍Deepfake technology can be a dangerous tool in the hands of malicious users. It can be used for personal revenge, political interests, financial fraud, etc... Let's make it more clear by giving some examples:

-Deepfake technology can harm people's private and work life. An ex-emotional partner or an employee can create an inappropriate fake video by using the person's face and voice. Offensive statements, sexual videos, and inappropriate comments about employers can be created and published online to offend a person's prestige.

-Deepfake videos can manipulate society by using public figures' images and voices. Spreading fake news, creating fake donation campaigns, and directing people to bad investments are some goals of this kind of manipulation.

-Deepfake content can also be used to fraud online banking systems. Identity thieves can go into the systems by using a person's image created by deepfake.

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How will we combat this threat? Can we fight against this developing technology, or did we already lose?

‍We learned that this technology grows really fast. So how will we combat this threat? First, we must accept that we can not trust our human eyes. That is too risky. We need to work with AI and direct it in the right way.

To combat our enemy, we need to know its weakness well. For example, deepfake technology creates very successful content when the face looks directly at the camera. But it is not as much as successful when the face looks another way. So there is a simple measurement to take: demand head movement to face recognition. Because in the synthetic videos created by deepfake, the image loses its clarity, especially around the nose, eye, and mouth. And this corrosion gives important tips to detect fake content.

Is knowing these tips enough to detect all deepfake content? Of course not! Because your AI can only learn what you teach. It can not catch a new method you didn't give it as data. Or a new technology can defeat your focused studies. So what do you need? You have to stay updated and feed your system with more and more data. Is it sustainable? Yes, but only if you have good partners and customers.

Now let's discuss this further. Is it possible that deepfake would reach a point that can not be detectable anymore? I want to look at human history to answer that. Historically, there were always frauds, and always some other people who fight against frauds. These two sides helped each other develop. So when the frauds found new methods, the protector found others. Personally, I don't think that an undetectable deepfake method is possible. I don't also see a future for deepfake, it will be replaced by generative ai in a short time. Generative ai will be able to create any content with a given face and text. That means you can make anyone tell anything in a fake video! And this content will not be synthetic, and these given tips will be useless. So what will we do in this scenario? How will we understand which video is fake? I don't have an answer to these questions yet. But if there is a lock for a door, we can always find a key to unlock it!