Deepfake Technology uses artificial intelligence called deep learning to present images of fake events and videos naturally. Deep Fake is an artificial intelligence called deep learning that makes fake event and video images look natural, creating fake photos from scratch, using voice skins, or cloning public voices. DeepFake has become a severe problem in many industries. But by detecting deep Fake, companies can easily detect fraud and protect themselves from unprecedented damage to brand reputation, customer data, and financial losses.
History of Deepfake Technology
If you follow the origins of the term Deep Fake, you’ll be surprised that it came from the world of Reddit. One user used it as his name. Today, the term is evolving to encompass all contents that fall into the category of synthetic media. AI technology called deep learning can be used to create images and videos that have different portraits of a person compared to the original.
However, this technology concept was born long before Reddit was born. In the late 1990s, an academic paper researching deep fake concepts presented a program that is the first example of what is today called deep fake Technology.
DeepFake was based on previous studies, such as facial analysis, text-to-speech synthesis, and modeling of human mouth movements in 3D space. Combining these three foci, the authors created what they call the Video Rewrite Program and synthesized new facial animations from the provided audio recordings.
After the publication of this academic paper, the research on this Technology faded into the early 2000s. However, as the new 10 years began in 2010, research reignited, focusing mainly on developing facial recognition functions.
One such application is a reproduction or clone of a person’s voice. This specific example of use has gained publicity thanks to intermittent headlines. One of the recent controversies over this Technology has been the cloning of the voice of renowned chef, travel documentary author, and writer Anthony Boudin, which has been used in documentaries about his life. People can use this Technology to bring back voices that are no longer there, but at the same time, it raises ethical questions.
What is deepfake Technology?
The word “deep fake” is a combination of two everyday words: “deep” and “fake.” Deep is an AI technology called deep learning. Deep fake Technology is used to create counterfeit content in synthetic media, replacing or synthesizing faces, speech, and emotions. It is used to digitally imitate actions that the person has not done. Academic institutions took the first step in this Technology in the 1990s, and more people have since adopted it. The creation of deep fake programs is not mainstream, but nevertheless, the concept has had a great response in the media space. In the next section, we will explain how deep fake content is developed.
Deep fake content is AI-generated video, images, and audio that mimics the appearance and voice of a person (synthetic media). The most popular deep fake videos are from TikTok user DeepTomCruise:
It is not a completely new technology. In fact, it’s been around for years in Hollywood movie studios. Still, it is now made available to a large number of people through commercial applications, and the amount of content circulating on the web is increasing. However, Facebook banned deep fakes in 2020 (except those that are clearly parodies).
Deep Fake Mechanics
In terms of what is behind Deepfake Technology, the name deep learning has a hint. The DeepFake algorithm absorbs data, learns from it, and creates new data by superimposing facial expressions and whole faces. Developers of deep-fake software typically use one of two types of ANNs: an autoencoder and a generative enemy network (GAN). The autoencoder learns to duplicate a large amount of data given, mainly photographs of faces and facial expressions and reproduces the requested data set. However, it is rarely an exact copy.
On the other hand, GAN has a smarter system, including a generator and an identifier. The generator reproduces the learned data as a deep fake and deceives the latter. The identifier compares what the generator creates with the actual image to determine its effectiveness. Of course, the best deep Fake is a perfect imitation of human behavior.
So, how can we use this Technology to create a deep fake?
The algorithms behind apps like Reface and DeepFaceLab are constantly learning from the data that passes through them so that they can effectively adjust facial features and facial expressions and overlay one face on another.
The software is basically a video editor specifically designed for working with faces. Some apps are more complicated, but overall, you can do anything from aging someone or not aging someone to editing yourself in a movie.
However, this Technology still has its drawbacks. Creating deep fakes may be more complicated than creating fake live videos, but they’re just as easy to spot.
How is Deepfake Technology different from Photoshop or face swap?
Fake and processed imagery, recently seen everywhere on the Internet, is often harmless. You’ll know the interesting effects of “face swap” with Snapchat and other photo apps, where you can paste other people’s faces on your own face or vice versa. Or maybe you’ve taken part in the trend of “growing old on your own” and showed your face at the age of maturity through a fake app.
Apart from the fact that these photofabrication techniques are designed for entertainment, they are almost harmless because it is easy to see that the images are fake and do not really reflect reality. That’s what makes deep fake dangerous. Using deep learning to create false images often creates a world in which no human can tell that an image or video is fake.
How do you distinguish a deep fake video or photo from the real thing?
Some deep fakes are so persuasive that it is almost impossible to distinguish them from the real ones. However, you may be able to tell where you are looking for the Deep Fake photos and videos.
The following are some of the signs to watch out for to distinguish deep fakes:
1. Unusual lighting and other factors
When AI generates photos and videos, it tends to misilluminate the lighting effect, resulting in an unnatural expression. AI is also famous for crushing fingers and hands. You may also notice blurry features, unnatural skin color, and parts that appear to “blend” into the image.
2. Unrealistic setting
Let’s think about the entire image. Is that setting appropriate for the person who appears? Often, AI is replacing much of the face but not the environment or other details. For example, if there are few pedestrians in a normally busy city, the photos and videos may provide clues that they are fake.
How to make a deep fake?
Creating a deep fake is surprisingly easy. It has a wealth of smartphone apps, and 80% of the population has easy access to deep fake creation. In addition to apps, there are also computer programs that can perform far more advanced reproduction with a local CPU (computer processing unit) or the best reproduction with a GPU (graphics processing unit).
Here are some of the most popular tools:
FaceApp: FaceApp can transform photos to add or remove features, so users can make celebrity-like photos in just a few clicks.
Wombo: Wombo creates song and dance video clips from user-uploaded photos.
DeepFaceLab: DeepFaceLab PC software is used to generate 95% of deep fake videos.
First Order Motion Model: The First Order Motion Model is a GAN-based PC software that can be used to swap faces, clothing, and more.
What are the other concerns of Deepfake?
The concern is not limited to deep fakes. Morph is a type of biometric attack technique that combines the faces of two or more individuals into one unique face. Since the morph may include elements of the face of an authorized user and the face of an unauthorized user, there is a possibility that the access is illegally provided by being deceived by face authentication. Morph can also be used to forge identity documents, such as passports, for individuals who cannot legally obtain a passport or cross a border. In this case, the portrait of the person whose passport cannot be acquired and the portrait of the person who can be acquired are combined to create a morph. With this morphed image, you can apply for a new passport. Once a passport is received, unauthorized travelers can use it to bypass the border controls.
News about deep Fake, morph, and other evolving potential threats, despite the pessimistic potential, is not all bad. One is that unsupervised learning, which is being perfected in deep-fake algorithms and applications, has great potential. Machine learning, such as that seen in Deep Fake Technology, helps autonomous vehicles recognize situations around them, including pedestrians, and helps improve voice searches and virtual reality applications.
One of the reasons DeepFake has been attracting attention to celebrities and historical figures is that there is plenty of background data available about them. Machine learning algorithms, such as those used for deep fake processing, must have a deep understanding of what the subject looks like. That is, you have to analyze the data of the subject from different angles, different lighting, and different conditions. In other words, it is difficult for ordinary people to obtain sufficient background data to become the target of a deep-fake attack.
The news is also promising for organizations and individuals that may use biometric authentication to ensure the security of their assets. The best-in-class biometric framework uses live detection. This will determine whether the user is a living person who is presented live on the imaging device or a presentation or impersonation attack designed to break through the system. Whether it’s a simple photo impersonation, deep Fake, or morph video, a reputable biometrics authentication system has a solid ability to distinguish between a live human and a live human facsimile.
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