Deep Fake AI: Everything You Need To Know

If you're unaware of AI, you must be living under a rock because artificial intelligence has taken the world by storm! AI is developing at the speed of light, and the latest innovative deep learning technology that is turning heads (literally) is **deep fake AI.

AI can be used for both good and bad, but how has deep fake impacted the virtual world? What is at the core of this technology, and how does this advanced form of AI alter reality?

Read on to learn all about this state-of-the-art technology and its implications.

What is AI Deep Fake?

Have you ever seen a movie where an actor's face is swapped with someone else's? This is precisely what deep fake AI does. It’s a form of artificial intelligence that uses deep learning algorithms to solve problems using large data sets. Even the name, 'deep fake' is derived from combining deep learning and the fake outputs it creates!

Deep fake is used to swap faces, bodies, and voices, creating convincing but fake videos and audio. In fact, Gartner, a leading tech research firm, predicts that by 2026, 30% of all companies will consider identification and verification useless because of increased AI deepfakes.

Deep fake AI is often used to change videos and audio to say or do things people have not done or said. However, it can also be used to create original content.

How Does Deepfake AI Work?

So, how does this captivating technology work? Often called the 'AI that deceives, 'deep fake content is created when two complex competing algorithms are used together. This creates the **Generative Adversarial Network (GAN).

The two competing algorithms are:

• Generator: The generator acts first by building a training data set, which creates the first set of fake content. It then passes it on to the discriminator.
• Discriminator: The discriminator identifies how realistic or fake the created data looks. It then passes this information back to the generator, which can refine the output so it looks more realistic.

These two algorithms combine to form the GAN, which uses deep learning to study and identify different patterns in images to create refined fakes.

When creating deep fake images, the GAN system views the image from different angles to assemble perfect fakes.

When creating deep fake videos, the GAN studies the video, movements, and behavior patterns from different angles to create a fake video.

Deep fake Video

Deep fake videos can be of two types:

Source Video Deep fakes

In this method, an autoencoder analyzes the target's facial expressions, gestures, movements, and patterns from an original video. The autoencoder then imposes the target’s specific expressions and movements onto the video that is to be altered.

Face Swap

Another way deep fake videos are made is using the face swap technique, where the target's face is swapped onto someone else’s body, making them do or say things they didn't actually say.

Deep fake AI Voice

Audio deep fakes are becoming very common and Natural Language Processing is used to create them. The NLP algorithms clone the target voice by identifying outstanding patterns and characteristics.

These patterns are then used to clone the voice and the programmers can make it say anything they want. The patterns are identified using samples of the target voice. The more samples used for creating the clone, the more accurate the deep fake audio will be. The whole process is called voice cloning.

Audio deep fakes are used in the gaming niche, the radio industry, and for streaming purposes.

Lip Syncing

Lip-syncing is one of the most common ways of using deep fakes. It uses voice recordings along with visuals to give the impression of speech. It can be highly deceptive, especially if the audio is also a deep fake.

Technology Used to Develop Deepfake AI

It’s becoming easier for the common man to use the latest technology to develop video, audio, and photographic deepfakes. Let's take a look at the various online platforms and technology required to make deep fakes:

GAN

As discussed above, the GAN combines two unique algorithms to create deep fakes. The GAN is crucial to deepfake AIs and is used in every deepfake created.

Convolutional neural networks, or CNNs

CNNs are used for facial recognition and to analyze various movement patterns.

Autoencoders

Autoencoders identify the specific characteristics of the target, such as a pattern of behavior, specific movement, or facial expressions. They then merge these into the deep fake video.

Natural language processing

As previously mentioned, NLPs identify the attributes of the target's speech and use them to create original text.

High-performance computing

High-performance computing, or HCP, refers to the supercomputers that run AI algorithms required to create deep fakes.

Uses of Deep fake AI

Deep fake AI has carved out a niche for itself in the rapidly advancing digital world. Following are some fields where deep fakes are used:

Entertainment Industry

AI has left an indelible mark on the entertainment industry. It’s used vastly to clone actors and their voices, to produce scenes that are hard to shoot during post-production if an actor is unavailable, or simply to save production time.

It’s also used in the music industry to create new music with the voices of existing singers. There have been several deepfakes of famous actors like Morgan Freeman, Tom Cruise, and even singer Taylor Swift.

One such video of actor Morgan Freeman on YouTube looks almost real. It has garnered millions of views.

Customer Response Services

Several companies use audio AI to create voices for simple tasks related to customer services, account management, troubleshooting, and filing complaints for banks and other businesses.

Audio deep fakes are used extensively in the customer support industry to help answer questions, troubleshoot, and increase customer satisfaction and retention.

It can also help increase customers by communicating in different languages. For example, Synthesia, the piano keyboard training app on Play Store caters to over 40 different languages!

Fraud and Blackmail

An unfortunate use of deep fake AI is blackmail. It’s often used to defame and falsely accuse the target of extortion. The most common example is when the target is put in explicit and inappropriate situations.

Some high-profile examples of blackmail using deep fake:

• Ukrainian president Volodymyr Zelensky was a victim of deep fakes when a video circulated in which he told his troops to surrender to Russia.
• To influence the US elections, President Joe Biden became the victim of deep fakes wherein he displayed cognitive loss.
• Facebook founder and owner Mark Zuckerberg became a victim of deepfakes when a video showed him mocking Facebook users and proclaiming that he 'owned' them.

Deep fakes have also been used to commit identity theft and bank fraud.

Impersonation

Unfortunately, deep fake AI has also been used for impersonation. Several scams and crimes have been committed using this technology when people have successfully impersonated someone else and robbed them of large sums of money.

A recent example of such fraud is when a multinational company in Hong Kong claimed a loss of $25 million. It happened because of a deep fake driven video impersonating the company’s chief financial officer via a video conference. The video fooled the employees into transferring money into the fraudster's bank account.

False Evidence in the Court of Law

Many criminal-minded people have also been known to use deep fakes to create false evidence to present in courts.

For example, AI deep fake can be used to study and clone the facial expressions and actions of a criminal. Then, it can fabricate an alibi and create fake videos, showing them present somewhere else, away from the crime scene.

Spreading False Information or Disinformation

Many times, deep fakes of politicians or other trustworthy personnel may be created, which are used to spread disinformation among the public. This leads to the spreading of false news which is a way to misguide the public.

For example, an image of the Pentagon emerged on social media in early 2023, that showed an explosion had occurred near the building. This was an image made using AI and was fake. It leads to a drop in the stock market due to the spread of fake news.

Defamation

Defamation is another way of spreading false information about someone, either written or verbal. Deep fake AI has been used carelessly in this regard.

A recent example of this fraud involved prominent English journalist Martine Lewis, who brought a lawsuit against Facebook for promoting online Ads with his name for bitcoin-related investments that he never endorsed. Although a third party ran these Ads, Facebook had to pay up to settle the dispute.

Can Deep Fake AI Prove To Be Dangerous?

Although deep fake AI is legal, it can still pose a grave threat to society. Deep fakes are only illegal if they break laws such as defamation, child pornography, or hate speech. So, while treading the fine line between legal and illegal, how do deepfakes cause harm?

• Defamation and blackmail.
• Spreading of disinformation and false news.
• Inciting the public and causing harm
• Manipulation of the stock market to decrease stock prices.
• Creating fake audio and video evidence to misguide the law.
• Fraud involving identity theft and taking over bank accounts.

Overall, deep fake is not all bad, but the bad uses of deepfakes are concerning. And at this stage, the bad uses overtake the good uses so we have to be careful when evaluating such false information.

Positive Use Cases of Deep Fake

Let’s take a look at how deep fake is being used for betterment in various sectors of life:

• Bringing characters back to life in movies to enhance audience interest.
• Using lip synchronization to automatically translate educational or resourceful documentaries into multiple languages.
• Helping teachers deliver more engaging lessons as it can bring historical figures to life in a classroom.
• Creating deep fake presenters or news reporters for better audience engagement.
• Creating deep fakes of celebrities (with their permission) to spread awareness about diseases or pandemics in different languages.

AI Deepfake Detection

So, with deep fakes being so prevalent online, how can you protect yourself from becoming a victim? There are several practices you can adopt to detect AI; here are some of them:

• When you zoom in or magnify a deep fake video, you might be able to decipher the poor lipsynching or inconsistent gestures of the people involved.
• The movements in the videos will seem awkward and misaligned.
• Deep fake images will contain uneven coloring, awkward body positions, and postures.
• Audio will be inconsistent, with pauses in random places and background noise.
• In deep fake videos, the people involved will not blink.

So, can AI detect deepfakes? The popularity and rise of deep fakes have pushed several multinational tech companies to invest in technology and tools that can detect AI.

Isgen is at the forefront of promoting the safe use of AI. We are currently working on developing technology to identify audio and deep fake. It’s going to be a huge landmark in ensuring ethical use of deep fake technology.

Summing it up

Are we heading towards an online pandemic? The use of deep fake AI in a fraudulent manner for the purpose of blackmail or identity theft is increasing. In the wrong hands, any technology can be used to harm individuals and society at large. Deep Fake is a prime example of such technology.

We must ensure that the public is aware of the harm AI can cause, and that cheap AI detection tools are developed to combat people who use AI negatively. Many leading tech firms are working on developing advanced tools to combat AI. But for now, it looks like humans may be losing the battle to AI.