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What Are DeepFakes And How Dangerous Are They?

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Did you hate the last season of Game of Thrones? Most probably! Did you also watch the online video of Jon Snow saying sorry for the horrible script of season 8? That video obviously wasn’t a real video as Jon Snow knows nothing! It was actually a fake but it looked so real. That’s because it was a DeepFake video made using Artificial Intelligence. Are you wondering what DeepFake is? Let’s check it out! What-are-DeepFakes-and-How-Dangerous-are-TheyDeepFake uses Deep Learning, a subset of Machine Learning, to create videos that look real but are actually fake. It is basically a technology that can replace the face of a person in an image or a video with so much precision that it looks real. Or it can make a person say something on a video that they never actually said in real life. “What you see is what you get” is no longer true on the internet because of DeepFakes. And that is how you can see Jon Snow blaming the last season of Game of Thrones when this never actually happened!

How DeepFakes Are Made?

A neural network algorithm known as an autoencoder can be used for creating DeepFakes which attach a fake face to an original face. Suppose you want to attach the face of Jon Snow to Tyrion Lannister. To do that you take thousands of collected photos of both Jon and Tyrion and run them through a neural network called an encoder. This encoder will study the facial features in both faces and compress the images into the standard features they both have. 

Then a neural network called a decoder will take these compressed images and recover the face of Jon. Similarly, another decoder will do this for Tyrion. To get Jon’s face on Tyrion’s face, all you have to do is take a compressed image of Jon’s face and feed it into the decoder that was trained on images of Tyrion. 

So, this decoder will reconstruct Tyrion’s face but it will use all the mannerisms and expressions that appear on Jon’s face. This has to be done on every image frame in a video to show Tyrion Lannister actually looking like Jon Snow. Which is very weird to imagine! Another method to create DeepFakes uses a Generative Adversarial Network (GAN)

This basically involves two neural networks that are known as the Generator and the Discriminator respectively. The Generator creates fake images using a data set of existing real images like celebrities. Then the Discriminator tries to catch any defects in the generated images. The fake images created by the Generator at the beginning are obviously fake and don’t even look like faces but with multiple passes, a real-looking fake image can be created. 

This is a classic case of “Fake it till you make it!”. In fact, it is even easier to create fake images if there is a large training data set of images already available. That is why celebrities and famous people are targeted the most. They have the most images and videos out in public.

How DeepFakes Work?

As discussed above, Deepfakes replicate the data of faces and expressions which cannot be easily identified by a human eye. Whereas, GAN produces data that learns from deep learning and uses generators and discriminators. 

Today there are apps like DeepFaceLab that learn by themselves from the provided inputs along with the facial expressions layer by layer and accordingly manipulate the data. In short, it can also be considered as a video editor tool that can be masked with the original video and replace the desired faces.

Although it will take some more time for accuracy it is heavily being used in movies and entertainment industries. Besides this, marketers are also trying to experiment with different promotional content without hiring any models/actors. You may also find some minor project apps that are based on Deepfakes technology which can be used to swap faces and do create some funny videos.

How DeepFakes AI Works?

The algorithms used in Deepfakes are based on artificial intelligence that is capable of masking the faces or manipulating the videos. It simply uses the GAN techniques that include both autoencoders and generators and learns from the given inputs, based on which the AI generates the output.

A report suggested that the total number of Deepfakes videos was 15,700 in 2019 which jumped over 85k by 2020 and crossed 100k by the end of 2021. Over time, technology has changed and improved the quality, but as per the experts, it will take years to achieve more accuracy.

How DeepFakes Pose a Cybersecurity Threat?

As we’re moving forward, technology is getting more advanced and is widely accessible in the entire world. Take the example of smartphones, from keypads to touchscreen and from passcode to face unlock, we’ve witnessed it all. Nobody has ever thought that a small video can create chaos if projected in the wrong way. 

There are certain videos available on the internet that are hard to identify whether it’s real or fake, however, attributes like facial structure, expressions, iris, etc. make them more realistic. This makes a clear indication that deepfakes are a potential threat to cybersecurity.

To handle such threats governments of different countries have made laws against this technology so that it cannot be used for the wrong purpose. In 2019, WIPO (Word Intellectual Property Organization) submitted a draft that addresses the deepfakes issue and the measure to handle the challenges.

How Do You Do DeepFakes?

As discussed above, deepfakes run of neural network type (autoencoder) which decreases the image’s latent space and reconstructs it from the provided input. In this, the AI trains itself on how the manipulation has to be done and then copies the elements of the key features such as facial expressions, posture, etc. The processing is being done in frames for each element like talking, iris movement, body action, etc. 

For such activity, a large set of data is being fed into the deepfakes so that their AI-based algorithm start processing which later converts the desired output into two chunks i.e. motion estimator and video generator.

Motion Estimator – It is used to perform the analysis of image sequencing and motion vector based on which the image transformation is being performed.

Video Generator – They use AI-based technology that makes realistic videos like human interaction.

Is DeepFakes Dangerous?

DeepFakes are used for everything currently: the good, the bad, and the ugly. There are many videos created using DeepFake that are just for fun and not going to harm anybody. Jon Snow apologizing for Game of Thrones is one such example! DeepFakes have also been used in films. One example is when Harrison Ford’s young face was inserted onto Han Solo’s face in Solo: A Star Wars Story. 

But more and more, DeepFakes are being used maliciously. According to an estimate, at least 96% of DeepFakes online are pornographic in nature where images of celebrities or other famous women are mapped on the faces of porn stars. 

This is a serious threat to many women. Another future threat of DeepFakes is the loss of trust. It is becoming more and more difficult to identify if a photo or video is real or fake. In this situation, it would become very difficult to trust anything as the truth. This can have huge ramifications. For example, courts would not be able to identify if a piece of evidence is real or fake in cases. 

Also, security systems that rely on facial or voice recognition could also be tricked using DeepFakes in the future. And could you ever be sure that the person you are calling on your phone is real or just a voice-and-face imitation using DeepFake?

How Do You Spot a DeepFake?

Unless you are an Artificial Intelligence algorithm, it is very difficult to spot a DeepFake! However, you can still do it if you look closely as they are fake after all. The most common sign is that the ears, teeth, and eyes of the person do not match the face outline sometimes. Lip syncing in the video may also be wrong and it is very difficult to create individual strands of hair in DeepFakes. And if the face appears too smooth to be real, chances are it’s not real but a DeepFake. 

However, it is getting more and more difficult to spot DeepFakes as they are looking more and more real with advances in technology. In such a situation, only Artificial Intelligence can recognize the use of Artificial Intelligence in photos and videos. Almost all big tech companies are investing in creating technology that can identify DeepFakes. 

One of the biggest efforts in this is the Deepfake Detection Challenge by Amazon, Microsoft, and Facebook which aims to identify fake content on the internet (which is getting more and more difficult to do!) Hopefully, all these measures will be enough to spot DeepFakes in the future. Otherwise, there may come a time when funny videos about Jon Snow would be the least of the DeepFake problems in this world. And even international catastrophes may happen because of the fake news spread by DeepFakes.

What About Shallowfakes?

Shallowfakes are less like deep fakes that use video editing tools to manipulate the content and show what’s not real to the human eyes. It is often termed as dumbfake which means that any content that is manipulated without using any deep fake technology. While comparing the contents of deepfakes and shallow fakes, it is way easy to identify what’s real and what’s fake but it poses the same threat that can be caused using deepfakes technology.

Conclusion

It is believed that deep fakes can cause a severe threat to society due to their ability to create fake content. Certain government bodies have made laws to address such issues. As we’re moving ahead in technology, the technology is getting sharper and all we need is to make sure that we’re not using it against ethics.



Last Updated : 01 May, 2023
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