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Deepfake Technology: The Ultimate Guide

I am a computer engineer, licensed digital marker, and content creator fluent in Arabic and English.

The pros and cons of deepfake technology

The pros and cons of deepfake technology

Deepfake Technology: The Pros and Cons, and How to Detect Deepfakes

"O, what a tangled web we weave when first we practice to deceive!"
– Sir Walter Scott

I do not doubt that when Sir Walter Scott wrote his famous poem "Marmion: A Tale of Flodden Field" and the above verse, he never imaged how it would one day become an evident reality.

We live in an age where "technology invades and prevails"; however, this slogan changed when another controversial technological innovation appeared. After the invention of deepfakes, we can now say that "technology invades, prevails, and misleads."

What Are Deepfakes?

In simple words, according to Dictionary.com: A deepfake is "a fake, digitally manipulated video or audio file produced by using deep learning, an advanced type of machine learning, and typically featuring a person’s likeness and voice in a situation that did not actually occur."

Everything You Need to Know About Deepfakes

This article will address the following topics and questions:

  • The History and Evolution of Deepfakes
  • How Are Deepfakes Created?
  • Pros of Deepfakes
  • Cons of Deepfakes
  • Deepfakes That Went Viral
  • How to Spot a Deepfake
  • The Fight Against Deepfakes
  • Is Society Prepared for Deepfake Technology?

The History and Evolution of Deepfakes

The term deepfakes originated in late 2017 from a Reddit user named "deepfakes," who, along with others in the Reddit community, shared these altered clips on the r/deepfakes subreddit. Many videos involved celebrities' faces swapped onto actresses' bodies in pornographic videos, while non-pornographic content included many videos with actor Nicolas Cage’s face swapped into various movies.

In January 2018, an application called FakeApp was launched. Using this app, you could create and share videos with swapped faces. In 2019, other deepfake applications appeared, such as Faceswap and DeepFaceLab, as well as web-based apps such as DeepFakesWeb.com.

Mobile application companies started to enter this market. Millions of smartphone users all over the world downloaded the Zao application, which allows users to superimpose their faces on television and movie clips with a single picture. The Japanese AI company DataGrid made a full-body deepfake that can create a person from scratch. They intend to use these for fashion and apparel.

The collaboration of audio deepfakes with AI software made it possible to clone human voices after five seconds of listening. In March 2020, Impressions, a mobile deepfake app, was launched. It was the first app for creating deepfake videos from mobile phones. This was just the beginning.

How Are Deepfakes Created?

Without going into headache-inducing details, deepfakes use deep learning technology, a branch of machine learning that's a form of artificial intelligence. It applies what is known as neural net simulation to massive data sets, creating amazingly convincing forgeries of video, audio, and photography.

The technology used is called generative adversarial networks or GAN. Deepfakes use facial mapping technology and AI that swap the face of a person on a video into the face of another person.

Apps and Websites Used to Create Deepfakes

Deepfake technology has taken the world by storm. It is evolving at an unprecedented speed and is becoming more and more available to everybody. There are many applications and websites used to create deepfakes, such as:

  • Zao
  • DeepFakes web β
  • Wombo
  • Reface
  • MyHeritage
  • DeepFaceLab

However, the most impressive deepfake examples tend to come out of university labs and startups.

Deepfake is one of the most controversial technologies. The release of deepfake videos generated a public turmoil of conflicting opinions as to whether the technology is good or bad.

Before presenting the benefits of deepfakes, I have to emphasize that the points I'm making are in regard to deepfake technology itself, not its founding artificial intelligence (AI) technology. AI has many more benefits and is increasingly becoming the backbone of all technological advancements.

Pros of Deepfakes

  • Video can be edited without the need for a reshoot.
  • An actor’s death doesn’t mean they can't continue shooting the movie.
  • A UK-based health charity used deepfake technology to have David Beckham deliver an anti-malaria message fluently in nine languages. His real words were actually in one language; the remaining eight were all deepfakes.
  • Last year, researchers at Samsung's AI lab in Moscow were able to transform Da Vinci's famous Mona Lisa into a video that shows her facial features moving.
  • A Scottish company, CereProc, was able to train its own deepfake algorithms on audio recordings of former US president John F. Kennedy. They were able to create "lost" audio of the speech JFK was going to give in Dallas on November 22, 1963, the day he was assassinated.
  • These new techniques, if applied in museums and art galleries, can definitely increase public interest in these areas.

Deepfakes in Medicine

Researchers use the generative adversarial networks (GANs), which are used in deepfake, to train AI to spot medical conditions and rare diseases by creating realistic enough medical images for AI to learn on.

In a paper by chipmaker NVIDIA, MGH & BWH Center for Clinical Data Science and the Mayo Clinic, researchers showed how they used GANs—algorithms that iterate and improve by competing against each other—to create synthetic brain MRI images with tumors. They used these data sets of brain MRIs to train the system.

The resulting images are so good that using a mix of 10% real data and the rest GAN-created was as good at training the algorithm to spot tumors in new images as a data set made up of all real images.

Both facial and body key points are critical in some medical diagnoses. However, data sharing for medical research is difficult as it is considered a violation of patient privacy. Traditional methods for face de-identification wipe out facial information entirely, making it impossible to analyze facial behavior.

The solution is proposed by deepfake technology since patients' faces could be swapped to a proper target face and become unrecognizable. This technique is still under research, but the results seem promising.

Cons of Deepfakes

  • The biggest threat imposed by deepfake technology is spreading false news. You can make anyone appear to say anything.
  • During the COVID-19 pandemic, some people created fake audio or video clips in which the voices and faces of famous doctors appeared, claiming that alcohol, excessive heat, or excessive cold kills the coronavirus, which was disastrous.
  • Deepfakes can rewrite history. This can be beneficial due to the fact that it raises interest and awareness, but on the other hand, history can be distorted and manipulated.
  • Financial scammers, con artists, and cybercriminals can use this technology's power to lure in more victims and steal critical information or infect their computers with malware.
  • Imagine the damage and manipulation that can happen when it comes to politics and political campaigns. How many fake videos—maybe even pornographic videos—could tarnish a politician's image?
  • Police and law enforcement authorities will suffer tremendously. A video can be faked to frame someone for murder, for example. Blackmail, identity theft, fraud are all examples of the crimes in which the line between truth and lies blur.
  • Fake images and videos can ruin relationships. This deception means that we will practically be swimming in a pool of lies. As deepfake apps become more advanced and as these apps invade the world of smartphones, it will be more and more difficult to detect truth from lies.

Recommended for you

The additional damage deepfakes can cause is yet to be explored. Every day, more people are using this controversial technology.

Deepfakes That Went Viral

  • A deepfake of Tom Cruise on TikTok
  • Obama’s public service announcement
  • Zuckerberg speaks frankly
  • Salvador Dali comes back to life
  • Will Smith as Neo in The Matrix
  • Jim Carrey in The Shining

And more . . . just go to Google, search, and explore.

How to Spot a Deepfake

The MIT Media Lab, one of the world’s leading research and academic organizations, hosted a website called Detect Fakes that displays thousands of these curated, high-quality deepfake and real videos publicly.

They defined some steps to identifying deepfakes. These steps are:

  1. Observe the face closely to determine whether it appears to have been manipulated.
  2. Check to see whether the facial skin appears too smooth or wrinkly, and determine whether the skin, hair, and eyes match in terms of agedness.
  3. See whether shadows appear where they should, as deepfakes often defy natural physics.
  4. See whether there's glare on any glasses present and if the angle of the glare changes based on the person's movements.
  5. Determine whether the subject's facial hair appears real or fake.
  6. Check to see whether moles and facial abnormalities appear real.
  7. Does the subject's blinking appear normal? See whether they're blinking too much or not enough.
  8. Observe the shape and color of the lips to determine whether they match the rest of the person's face.

Detection technologies are advancing quickly; however, unfortunately, so are the quality of deepfake videos, which makes detection more and more difficult.

The Fight Against Deepfakes

Many industry giants have begun taking serious steps to protect people from deepfakes.

Social Media

Platforms like Twitter, Facebook, and YouTube have banned the use of malicious deepfakes.

The Deepfake Detection Challenge

AWS, Facebook, Microsoft, the Partnership on AI’s Media Integrity Steering Committee, and academics have come together to build the Deepfake Detection Challenge (DFDC). The goal of the challenge is to encourage researchers around the world to build innovative new technologies that can help detect deepfakes and manipulated media.

Deepfake Databases

Jigsaw, a technology incubator within Google that builds technology inspiring scalable solutions to threats to open societies, released a large dataset of visual deepfakes in 2019. The resulting videos, real and fake, were a contribution that directly supported deepfake detection efforts. As part of the FaceForensics benchmark, this dataset is available and free to the research community for use in developing synthetic video detection methods.

The Deepfake Detection Challenge (DFDC) shared a dataset of 124,000 videos that featured eight algorithms for facial modification. The goal of the challenge was to encourage researchers worldwide to innovate new technologies to help detect deepfakes and manipulated media.

Software

Adobe, in collaboration with scientists from UC Berkeley, used machine learning to automatically detect manipulated facial images.

Microsoft created two pieces of technology; both aim to provide readers with the necessary tools to differentiate what's real and what isn't. The first is Microsoft Video Authenticator, which analyses images and videos to give "a percentage chance, or confidence score, that the media is artificially manipulated," per a blog on Microsoft's official site. The second piece of technology allows creators to add "digital hashes and certificates" to images or videos.

Legal Solutions

Deeptrace is a firm that provides an artificial intelligence-based anti-fraud solution. It is used to detect suspicious inconsistencies in images and videos, similar to a deepfake antivirus.

Government Intervention

U.S. Defense Advanced Research Projects Agency (DARPA) is funding research to develop automated screening of DeepFake technology through a program called MediFor, or Media Forensics.

In the U.S., legislation has been introduced to address deepfake technology at a state and federal level; although manipulated political images and media is not an entirely new concept, deepfake technology has taken the potential for manipulated media to a new level.

There are more efforts done and initiatives every day to facilitate deepfake detection.

Is Society Prepared for Deepfake Technology?

What does the future hold for deepfakes? To put it in simple terms, GAN-generated faces are near-impossible to tell from real faces.

Professor Geraint Rees, Pro-Vice-Provost of Artificial Intelligence at UCL, writes that AI can and must be used for good to complement and augment human endeavor rather than replace it.

The problem we are now facing is that society is not currently prepared for the threats deepfakes can pose. From policymakers to ordinary citizens and consumers of media, we all lack the knowledge and tools necessary to fight such a threat. The implication of trusting whatever you see in the age of deepfakes can lead to catastrophic results. So the two final pieces of advice I can offer are:

  1. Train your eyes to spot deepfakes (especially now, before they evolve).
  2. Please be skeptical; double-check whatever you see or hear.

Let’s all keep our fingers crossed that the development of deepfake detection endeavors will be faster than deepfake improvement efforts.

Sources

This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional.

© 2021 Nahla Roshdy

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