Deep Fakes - Everything You Must Know About It

By Haseeb Awan

Today, with the help of modern technology, it is hard to believe that anything is still untouched. It has simplified our lives. People have utilized technology for a variety of reasons. On the other hand, technologies were not only created to benefit people. deep fakes, for example, might create severe issues and confusion in society.

deep fakes, in many cases, appear to be clever marketing tactics or harmless memes. deep fake technologies are growing political, cultural, social, and economic risks with the capacity to inflict damage.

The consequences of deep fakes are both alarming and dangerous. They range from false information to ruining a person's or organization's reputation to corporate espionage and cyberattacks. Even entire communities popping up will create custom deep fakes for whoever is willing to pay the price.

What is a Deep Fake?

A deep fake is a fraudulent digital representation of a natural person. deep fakes are images or videos that have been changed using artificial intelligence to swap the person in the image with another person.

deep fake technology has been around for several years, but it has only recently begun to gain mainstream attention. The term "deep fake" was firstcoined in 2017 by a Reddit user who used artificial intelligence (AI) to create realistic fake videos. deep fakes use AI algorithms to generate or manipulate audio and video to create realistic-looking or sounding fake content.

Some recent, more well-known examples of deep fakes are a voice sounding like your boss on the phone, Facebook's Mark Zuckerberg appearing to say how great it is to have such a vast amount of data, and Belgium's prime minister saying the Covid-19 pandemic is linked to climate change in an edited speech.

What Are They Used For?

In September 2019, AI firm Deeptrace discovered 15,000 deep fakes online, an immediate increase of over ten months. An overwhelming majority of deep fakes are pornographic, almost 96%; among them, 99% of the mapped faces are swapped with porn stars. This is indeed disturbing.

In 2019, several prominent voices joined in on the debate over deep fakes. "deep fake technology is a weapon used against women," said Danielle Citron, a law professor at Boston University. Revenge pornography is likely to migrate from the celebrity world into our daily lives. Citron added: "It will be used for political and commercial gain."

Deep Fakes Statistics

As of June 2019, the number of deep fake videos detected by IBM was only 3,000. In January 2020, that number had increased to 100,000. As of March 2020, there are over one million deep fake videos on the internet.

According to a study by Deeptrace, in December 2018, there were 15,000 deep fake videos. This number increased to 558,000 by June 2019 and jumped to over one million by February 2020.

The majority of deep fake videos are created for pornographic purposes. In a study by Deeptrace, 96% of all the deep fake videos they found were non-consensual pornography featuring women.

Is It Limited To Videos Only?

deep fakes can't make photographs from scratch. deep fake technology may create compelling but completely fake images from the ground up. It's feasible that "Maisy Kinsley," a nonexistent Bloomberg journalist with a Twitter and LinkedIn profile, was a deep fake. A LinkedIn fraud named "Katie Jones" claimed she worked at the Center for Strategic and International Studies. However, she is considered a deep fake created for an overseas espionage campaign.

deep fakes are video and audio recordings that can be used to generate "voice skins" or “voice clones” of well-known people's voices. In March, the CEO of a British subsidiary for a German energy firm paid nearly £200 000 into a Hungarian bank account after being duped by someone who perfectly imitated the voice of the German CEO. The company's insurance broker suspects it was a deep fake recording, but there isn't enough evidence to make this conclusion conclusively. There have been several situations in which fraudsters utilized recorded WhatsApp conversations to defraud people.

How Does Deep Fake Technology Work?

deep fake software may be made with several machine learning methods. Simply put, these algorithms can make stuff based on the information supplied. If a program is tasked with generating a new face or replacing part of a person's face, it must first be trained. It is fed vast amounts of data and taught to create new data for the process to work. Autoencoders and generative adversarial networks (GAN) are frequently used. Let's look at what these techniques are and how they function.

Autoencoders

The goal of autoencoders is to copy their input, which means they're a self-supervised neural network built on dimensionality reduction. They are data-specific, implying that autoencoders can compress information similar to what they've been trained on. In addition, the output from an autoencoder will not be identical to its input.

An encoder, code, and decoder are needed to build an autoencoder. The encoder shrinks the input data and fabricates the code after the decoder recovers only the input using the code. There are various types of autoencoders: deep autoencoders, convolutional autoencoders, contractive autoencoders, etc.

Generative Adversarial Networks (GAN)

GAN models learn from input data and generate new, similar data. The system is trained using two different neural networks: a generator and a discriminator. The generator extracts patterns from the inputted information and trains itself to reproduce them. The generated data is then sent to the discriminator, which also receives accurate data. The goal of the generator is to trick the discriminator. You keep training until the discriminator can no longer tell fake data from actuality. If it becomes difficult for human users to discern between generated and accurate data, you've done an excellent job training your system! GANs require more time and effort during training than other methods but usually produce better results-- especially with photos rather than videos.

Deep Fakes: What's the Science Behind Them?

Data scientists wanting to learn more about deep fake technology and its implications for government entities, private enterprises, public safety, and cybersecurity can gain much knowledge by studying deep fake methods and their science. As artificial intelligence advances, those in power must learn to combat the dangers of fake news and other harmful synthetic media.

Thanh Thi Nguyen and colleagues wrote a paper called "Deep Learning for deep fakes Creation and Detection," which emphasizes how vital deep fake detection is. Government agencies and industry giants have invested in the advancement of deep fake detection because they understand how crucial it is.

In 2008, the United States Defense Advanced Research Projects Agency (DARPA) began a media forensics research project known as Media Forensics or MediFor to counteract the threat of this face-swapping technology.

deep fakes will become more essential as the technology improves and the potential for evil uses of deep learning grows.

Why Are People Into Deep Fakes?

From academics researchers and industrial enthusiasts to porn producers, and visual effects companies, everyone is using AI for all types of applications. For example, part of a government's online efforts to discredit extremist groups or contact targeted individuals may involve experimenting with the technology.

Should I worry?

Isn't it frightening? It's worrisome not just because such fakes have previously been used to produce porn involving performers but also because they might be utilized to influence public opinion during an election or pin the blame for a crime on someone else.

With the swiftness at which information travels via social media platforms such as Twitter, it has become increasingly difficult to discern real news from fake news. A study showed that people are more likely to spread fake news than real news.

Any false information, whether a deep fake or not, can rapidly spread across the internet before being removed. This causes unnecessary panic and mistrust among people who generally believe that seeing is equivalent to believing. Unfortunately, another consequence of deep fakes is that even celebrities or other significant public figures could deny prior actions if there were video evidence against them- since deep fakes look so incredibly real, anyone could claim that a clear clip is fake.

However, It's not nearly as bad as it sounds. deep fake technology is also being applied in a variety of fields, including education, film, entertainment, social media, games, digital communications, business, science, and healthcare.

The film business may profit in many ways from deep fake technology. It can improve amateur videos to professional quality, re-enact famous scenes from films, and create brand new movies with long-dead actors. deep fake technology can also be used to produce digital voices for people who lost their actual voices as a result of illness.

deep fakes can be used to help people cope with the death of a loved one by resurrecting a deceased buddy "from the dead." GANs are also being investigated to examine X-rays' anomalies and their potential use in producing virtual chemical molecules that would speed up material research and medical discoveries.

How To Spot A Deep Fake?

There are a few things to look for when trying to spot a deep fake. One is the quality of the video or image. If it looks too good to be true, it probably is. Another is to look for any telltale signs of manipulation, such as an uncanny facial expression or body movement.

Their audio can also spot deep fakes. If the person in the video says something that doesn't match up with their lip movements, it's likely a deep fake.

If you're still not sure, there are a few ways to check. One is to reverse search the image or video online to see if it's been doctored. You can also try searching for the person in the video to see if there are any other instances of them saying or doing the same thing.

If you think you've spotted a deep fake, you can report it to the platforms where it's posted, such as Facebook or YouTube. You can also report it to the website deep fakes Detection, which is working on a database of known deep fakes.

Can We Improve Deep Fake Identification?

Computers are not yet accurate enough to identify deep fakes with precision. The best software available is only 65-82% successful in identifying them.

deep fakes are also difficult to catch up with. deep fakes may improve as quickly as people learn; the Guardian observed that individuals used to examine a subject's eyes for lack of blinking to detect a deep fake. When news spread, producers started including blinking into their films.

Fortunately, it only took the directors three months to create a six-minute video on Nixon. Most people aren't going to put in this type of effort to make a movie that's this believable.

Is It Legal To Use A Deep Fake?

Artificial intelligence's application in filmmaking is still relatively new, and laws surrounding its use have not yet caught up with it. In many countries, it is not regulated at all. China was one of the first nations to pass a ban on deep fake technology. The Cyberspace Administration of China declared that deep fakes created using AI are illegal. Almost every state in the United States has legislation prohibiting deep fakes that harm candidates running for political office. Another bill bans deep fake material that might influence election candidates.

Will Deep Fakes Be The Downfall Of Society?

We may expect to see more intimidating, demeaning, debasing, harming, and destabilizing deep fakes. Will deep fakes cause international incidents? The answer isn't so black and white.

Despite their growing expertise, the ability to deceive is limited by new technologies. For example, the advent of HD facial recognition technology has made it easier for companies to detect when someone is using a deep fake. The technology can also be used to create "face prints" that can uniquely identify each person.

Deep fakes could also be used to exploit people sexually. In one chilling example, a deep fake video was created of a woman that looked like she was consenting to sex. This video was then circulated online without her knowledge or consent.

As you can see, deep fakes have the potential to cause a lot of harm. They could be used to spread false information, create chaos, and exploit people. It is important to be aware of this new technology and its potential dangers.

The Deceptive Technology of Deep Fakes Is Not Going Anywhere

As technology advances, deep fakes are becoming more and more realistic. With large datasets readily available online, anyone could become a target of malicious intent.

Inconsistencies are the most significant sign of a deep fake. If you're worried about your online reputation, only post trustworthy content and be honest about your brand. You can use deep fakes or social media sites to take down any reported posts. Reputation management companies will make the process easier for you.

Additionally, not every deep fake is malicious. Several legal and Ban-free uses exist among VFX studios, museums, and even the medical industry. And as laws continuously develop, social media companies are taking action to eliminate further any deep fakes made with bad intentions; these will eventually flourish.

Additionally, not every deep fake is malicious. Several legal and Ban-free uses exist among VFX studios, museums, and even the medical industry. And as laws continuously develop, social media companies are taking action to eliminate further any deep fakes made with bad intentions; these will eventually flourish.

What's The Best Way To Go?

AI might be the solution to this problem. Although artificial intelligence currently assists in identifying fake videos, many deep fake detection systems have a main flaw; they Excel when it comes to celebrities because there is an abundance of footage that these systems can use for training. Large tech companies are now focusing on creating new ways to flag fakes as soon as possible after they're released. Another potential method involves the media's authenticity and provenance. Digital watermarks aren't guaranteed to work, but if we switch over to using blockchain technology, we could create an online ledger system that would hold information like pictures, videos or audio recordings. This way, their origins along with any manipulations made afterwards could always be checked and confirmed.

The best way to protect ourselves against deep fakes is by being aware of their existence and critical of our online content. We should also avoid sharing personal data or images that could be used for creating a fake without our consent.

If you come across a deep fake, you can report it to the social media platform where it was found or to a reputable deep fake detection website. You can also contact the person who appears in the deep fake and let them know that their image is being used without their consent. Finally, you can contact the authorities if you believe a deep fake is being used for criminal purposes.

With deep fakes becoming increasingly prevalent in digital media, those who want to use the technology for their purposes need to consider how it will affect their current contracts and what laws they might have to follow.

Furthermore, people who sign talent contracts should look closely at the terms surrounding their publicity rights to ensure they have enough control over how those rights can be used in connection with AI-based technologies. If we approach deep fakes with care and thoughtfulness, they can benefit commercially and socially.

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