Artificial intelligence, machine learning, and computer programming have become incredibly advanced. They are now capable of creating near-accurate reality simulations. One such technology is deepfake, which is rapidly gaining popularity worldwide because of its capabilities and consequences from the use of technology.
The term “deepfake” comes from “Deep Learning” technology. It is a form of artificial intelligence that teaches itself how to solve problems with large data sets. It is used to swap faces and create video content that mimics a real person but isn’t them.
For example, look at these fake videos of Vladimir Putin and Kim Jong-un and see how eerily similar they are to real people. We’d have trouble believing these videos are made with deepfake technology if MIT didn’t convince us of it in this article.
How Did Deepfakes Start?
Data manipulation and alteration of information became commonplace with the advent of the internet. But they have always been a part of human nature since such activities were commonplace in wars and politics to misinform and mislead people.
The deepfake technology started as a video rewrite program by Christoph Bregler, Malcolm Slaney, and Michele Covell in 1997. The program manipulated existing video footage and replaced it with new and different content. It was the first time when a program completely automated facial reanimation.
Fast forward to 2014, Ian Goodfellow invented the Generative Adversarial Networks (GAN). The program allowed the creation of realistic fake videos like the Putin example above.
GAN works on the adversarial model process. The generative model G acquires the data distribution, and the discriminative model D estimates the probability of the sample. The model’s design is to maximize the likelihood of mistakes from D and by training the G. The whole framework has played a vital role in deepfake technology evolution.
How Do Deepfakes Work?
The deepfake working mechanism heavily depends on GAN and machine learning models. However, it requires a particular process for it. The most common method of creating deepfakes is using deep neural networks with autoencoders.
First, you need the target video to manipulate, and then you need a set of videos of the person in which every face angle is clear. The videos set can have unrelated clips in them, one can be a clip from a movie, and the other can be random clips or social media videos.
The autoencoders are a deep machine learning AI tool that studies videos. The purpose of studying videos is to know the person’s appearance from different angles and environments. The tool then maps the person on the individual in the manipulated video.
The process is improved with the help of GAN, which studies large data sets and develops highly accurate results. It fixed the flaws in facial replacement by detecting potential areas for improvement. It makes deepfakes even harder to detect and decode.
What Are Deepfakes For?
Realistic-looking synthetic videos have tremendous applications in the movie, gaming, and cinema industries.
However, it has a dark side too.
In 2017 a Reddit community called “Deepfake” was found responsible for creating fake pornography, also known as “revenge porn”, of celebrities and actors. The purpose was to violate privacy and distribute sexually explicit content without the person’s consent to damage their reputation.
What Are The Dangers Of Deepfakes?
Deepfake technology has several potential disadvantages and can be extremely dangerous in the wrong hands. Firstly, it can facilitate the spread of false information on a mass scale. Deepfakes are often used to create videos featuring fake news stories and political statements that have not been said by those presented in the video. This is particularly concerning during elections or other moments of high political tension, as deepfakes could easily mislead millions of people into believing false information and affect their voting decisions.
Additionally, deepfakes are also frequently used to spread malicious content, such as defamatory material or threats targeting specific individuals. By using someone else’s likeness without their permission, perpetrators can cause significant emotional distress and disruption to an individual’s life.
Furthermore, deepfakes can create “synthetic media” — generated images or videos that look and sound authentic yet have never existed before. So, anyone can be made to say or do something they never did without recourse for the person affected. In this sense, deepfakes threaten the reliability and trustworthiness of digital content.
Finally, deepfakes pose a significant threat to consumer privacy by allowing hackers to access personal data like photos and videos, which can then be used for identity theft or other malicious activities. With rampant misuse of this technology, it is only a matter of time before more serious consequences arise.
One such example of deepfake dangers is a fake video of Mark Zuckerberg. Artists Daniel Howe and Bill Poster, in partnership with AI Company Canny, created a spectre video for an exhibition. They used a two-year-old clip of mark for “one man, with total control of billions of people’s stolen data, all their secrets, their lives, their futures.” Facebook now owns the video, and you can also find it in the bowels of Instagram.
This example shows how this technology can change the original content and cause mass misinformation and panic.
What Is A Shallowfake?
Shallowfakes can maliciously manipulate people’s minds and opinions about an event or person. The end result of shallowfakes and deepfakes are the same, and both tend to cause harm with manipulation.
Shallowfakes can use similar and realistic manipulation videos for harm and fun. You have to manually create shallowfakes, unlike deepfakes, which AI and machine learning can use. Although it requires heavy editing, it takes considerably less time than deepfakes. You can even cut and alter the video speed to change the clip.
What Is The Difference Between A Deepfake And A Shallowfake?
Shallowfakes are videos, images, or audio recordings that have been slightly manipulated to misrepresent information. They can be created by changing minor details in existing footage or audio, such as removing a few frames from the start of a video or adding a tiny bit of distortion to an audio recording. This type of manipulation is relatively easy and takes very little technical skill.
Deepfakes are much more complicated. They utilize sophisticated artificial intelligence (AI) algorithms to generate realistic digital images and videos using source material from multiple sources. For example, deepfakes combine the facial features and expressions of one person with the voice of another to create a convincing composite image or video. Deepfake technology requires significant time, effort, and computing power to create compelling digital impressions. The results of deepfakes can be challenging to verify and have severe implications regarding misinformation and disinformation campaigns.
In summary, shallowfakes are easier to create than deepfakes and require less technical skill; however, deepfakes are more realistic and difficult to detect due to the advanced AI algorithms used in their creation. As such, deepfakes pose a much greater risk for widespread deception than shallowfakes.
How To Spot A Deepfake Video?
Deepfake videos have become increasingly convincing, making them difficult to detect even with the naked eye. However, certain tell-tale signs can help you spot deepfakes.
One key indicator is the video’s audio. In some deepfakes, the sound may be slightly out of sync or distorted compared to the original recording. This is often due to a mismatch between the video’s synthetic voice and natural lip movements.
You should also pay attention to strange pauses or unnatural speech patterns, which could indicate that a fake was used instead of real audio from the person depicted.
Another tip is to look for inconsistencies in lighting and shadows within the video footage itself. A sudden change in the direction or intensity of lighting can be a tell-tale sign of a deepfake video. Deepfake technology has advanced significantly in recent years, but it still may have difficulty recreating subtle changes in light and shadow that are present in real-world recordings.
It is also vital to examine the facial features of people featured in a potential deepfake video. Deepfakes use algorithms to map images onto videos, so they cannot perfectly replicate a person’s unique features like wrinkles, freckles, and moles. Artificial intelligence has advanced significantly in recent years, but it may still be difficult for some deepfakes to recreate these minute details of an individual’s face perfectly.
Learning how to identify deepfake videos takes practice, but being able to spot these videos is a valuable tool. Taking the time to review potential deepfakes carefully can help ensure that accurate information is shared and not compromised by deceptive videos.
As technology progresses, it may become increasingly difficult to tell the difference between real and fake footage, but with some practice, it’s possible to protect ourselves from being fooled. With this knowledge, we can all be better media consumers and avoid falling victim to false information.
Example Of Deepfake Videos
We have already mentioned Vladimir Putin and Kim Jong’s fake clip examples. However, the following are the popular deepfake clips
- Mark Zuckerberg’s deepfake video on Instagram
- Vladimir Putin has a message for Americans
- Kim Jong-un makes a mockery of American democracy
- Jordan Peele’s deepfake of Barack Obama
Are Deepfakes legal?
Texas was the first state to criminalize deepfake clips. Virginia law also made it illegal to manipulate media with deepfakes and other methods. A guilty person can face up to 1 year in prison and a $2500 fine.
The World Intellectual Property Organization has concerns over it, and they consider it a personal right issue as a violation of privacy and intellectual property.
How To Stop/Prevent Deepfakes
One way to help prevent deepfake videos from spreading is by increasing public awareness of their use and the potential consequences. Educating people on how to identify a deepfake video and report it is also essential.
Another way to stop or prevent deepfakes is for companies and organizations, such as social media platforms, to develop detection algorithms to identify these videos. These algorithms can then block or flag content before it has a chance to spread. Technology companies and researchers are also developing tools that allow people to authenticate videos so that any alteration can be detected quickly.
Finally, governments should consider enacting laws that provide penalties for those who create or share deepfakes with malicious
Legal Approach
The most effective way to prevent deepfakes is to take legal action. The first step in this process would be identifying the person responsible for creating or disseminating the content. Next, the perpetrator should be served with a cease-and-desist letter, which requires them to stop producing and sharing any deepfakes of the targeted individual or company. Any further actions should only occur after consulting an attorney experienced in Intellectual Property Law and navigating through jurisdictional issues.
Additionally, filing a lawsuit against the creator of the deepfake can help resolve legal issues as well as seek damages for the harm caused by it. Depending on applicable laws, legal professionals may pursue criminal charges if deemed necessary.
Common Questions
- How do you tell if a video is a deepfake?
You can ask the person to come live in video, or you can see distortion in the facial features like eyes, nose, and lips. Moreover, there can be some incorrect duplication of accessories like
- What is a deepfake example?
We have some famous examples of deepfake videos of Putin, Obama, and Mark Zuckerberg. You can find plenty of examples and deepfake cases on the internet.
- What could a deepfake be used for?
Deepfake technology can be used for criminal activities like making fake videos, identity theft, revenge pornography, spreading misinformation, political unrest, and manipulation.
- Are deepfakes legal?
States and international organizations are coming forward to make matter laws and regulations against the misuse of deepfakes. However, such practices and awareness are sorely lacking.
- What was the first deepfake?
The origins of the deepfake tech go back to 1997, but it became really powerful in 2017 when an unidentified user on Reddit posted an algorithm that used AI to create lifelike fake videos. Others shared that programming to GitHub, where it became publicly available.
- What is the best way to avoid deepfakes?
Take legal measures, train computers, and social media platforms to spot fakes, and use blockchain technology.
- What is the difference between a deepfake and a regular fake?
Deepfakes utilize AI and machine learning, manipulating images and videos to create fake ones.
