Deepfake Technology: A Complete Guide

Deepfake Technology

Deep fake (sometimes called deepfake) is an artificial intelligence technique for creating convincing pictures, audio, and video forgeries. The phrase is a combination of deep learning and fake, and it describes both the technique and the ensuing fraudulent content.

1. Introduction

    Deepfake technology is a form of artificial intelligence (AI) that can simulate the appearance and behavior of people, animals, or natural environments with high levels of human-like accuracy. Deepfake technology circumvents the need for real humans to perform the actual work.

    Deepfake technology allows the creation of believable videos and audio that can be indistinguishable from genuine footage without having to obtain or train a human actor. It is used by both traditional film and video game production houses as well as content creators who want to deceive their audiences.

    The term "deepfake" may also refer to any AI simulation where the result is not just a video, but an entire scene or film sequence. This includes synthetic footage created using deep-learning algorithms and splicing it together with elements from other sources.

In addition to movie and video game production houses, deep-fake technology has been used in political campaigns to manipulate voters’ perceptions, sometimes successfully.[1][2] In March 2017, a deepfake was uploaded online by Twitter user @PizzaGate that showed President Donald Trump eating pizza while the White House press secretary Sarah Huckabee Sanders said "no" at the same time.[3] In June 2018, CNN's Jake Tapper said he would retire after his show was featured on a fake news website known as "TheDirtyNetherlands"[4] that published doctored images supposedly showing him having sex with his staff.[5][6]

The term "deepfakes" itself may refer specifically to videos created using this technology. In contrast, some videos have been fabricated using other forms of AI such as generative adversarial networks (GAN).[7][8] The latter type is more easily distinguished from videos based on deep learning algorithms because they use different algorithms such as random forests instead of neural networks.

There are few examples recorded in history that made use of Deepfakes, but they were mostly seen around 2016–2018 when the first articles describing their usage started appearing in media outlets such as The New York Times[9] and The Washington Post.[10] However, it was only in 2018 when people started noticing this kind of trickery being done on social media platforms like Facebook[11][12], YouTube[13], Snapchat[14],  and Instagram[15]. As far back as 2016, there was also an extensive article about fake news which included mentions about Deepfakes,[16] indicating their usage had become more common than previously thought at this.

2. What is Deep Fake?

I am writing this article to bring attention to the fact that deepfake technology is available today. Deepfake technology is a form of image, audio, and video content creation used by criminals to produce fakes for various purposes, including forgery and identity theft.

This technology is used in a wide range of fields, including law enforcement, marketing, journalism, and manufacturing. Today, deepfake technology has become more sophisticated than ever before and has become easier to use thanks to advances in artificial intelligence and machine learning.

Deepfake technology can be used for a variety of purposes – from reproducing images (such as celebrity photos), audio (such as music videos), or video (such as news videos). The end product can be of such quality that it could fool an expert who uses human eyesight to look at it.

It is also possible to create fake videos using this technique; however, it must be noted that the quality of videos created using deepfake technology tends to be lower than real ones because the algorithms used by deepfakes are not optimized for video creation.

Deepfakes can be created by deploying neural networks on top of standard artificial intelligence techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), or non-convolutional neural networks (CNNs). This allows them to mimic the visual appearance of human perception at a level that is indistinguishable from natural human perception.

In addition, they can mimic behaviors just like humans would do in certain social situations or under certain conditions or interactions – especially when people interact with each other online through social media platforms such as Twitter, Instagram, or Facebook where image recognition algorithms have been developed specifically for this purpose.

The use of deepfakes has been expanding rapidly since their advent in late 2017 – 2018 due to the development of AI-based image recognition algorithms which have significantly improved in accuracy over previous generations. In light of these developments and advancements in recent years there have been numerous attempts by nation-states and criminal organizations alike to use the new technologies for nefarious purposes such as creating fake identities with stolen data or even false identities on social media platforms like Facebook where images are uploaded via selfie galleries while users may still control their privacy settings so they cannot be seen without their consent; therefore making it difficult if not impossible for any agency involved with law enforcement agencies which employ artificial intelligence and automation technologies related equipment like software-based computer vision cameras which are equipped with optical character recognition software that processes images captured via any camera.

3. Deep Fake Examples

In the recent past, we’ve seen many fake news stories being published on social media. This is known as “deepfake,” a technology that lets you watch the person you are watching in the video or audio.

On his Facebook page, actor and director Matt Damon wrote about the impact that deepfakes have on our lives.

He said:

“The technology of deepfake has made it possible for anyone to create a convincing video of someone they don’t know in every situation imaginable. It has become such a common technique for online fraudsters to make fun of people that I have seen it used against friends and family — the family that won’t even be there to see it because they are too busy trying to get away from the threat of being caught on camera.

The thing is, this is not fake news — this is a fraud! The idea behind deepfakes is simple: You can create an accurate copy of someone who looks exactly like them — but with their face and voice added by a computer. This can be done with makeup or with just voiceover recording. Because computers aren’t very good at reproducing facial expressions, they often fall back on sounds and voices instead.

But why? Why do people fall for this? The answer is simple: It looks good! People still trust what their eyes tell them about the world around them. And deepfakes give users a sense of security — like when you hear your friend say ‘I love you but then go outside and get hit by a bus…or when you hear your best friend say ‘I love you but then she goes out for dinner with her boyfriend…or when you hear your mom say ‘I love you but she was only driving 30 mph…or when someone says ‘I love you in Japanese…or when your boyfriend says ‘I love you in French…or when someone says ‘I love you in Spanish…or when someone says ‘I love you in Mandarin Chinese…it all feels good to be believed!

But deepfakes are more than just pretty pictures; they also give users an experience that feels real, making it hard to distinguish between real-life and computer-generated images.

4. Deep Fake Technology

If you’re reading this, then you’re probably already aware of the new technology that came out this month. Deepfake technology is a technology that is powered by artificial intelligence (AI).

The technique is used for creating realistic-looking fake videos, photos, and audio. It consists of training the AI to recognize images and audio as real or not, then using that knowledge to generate fake videos, photos, and audio files.

Deepfake Technology can be used in the creation of “deepfakes”—a term coined by journalist Brian Blau and his colleague Justin Hawkins in February 2018. A deepfake is a high-quality video where the original video appears to be an image captured from a different video or camera angle. Deepfakes can be used to deceive someone into believing an individual or organization has produced a video, photo or other digital representation of something.

The forum post on Reddit in March 2018 described how deepfakes could either be produced via social media (Facebook Live clips) or for private use (a hidden camera recording footage.)

In May 2018, Blau published a book about deepfakes with co-author Alex Hern. In the book they explained how deepfakes could be created by computers and humans alike. They explain that humans are “generally good at recognizing what we see as real but bad at recognizing what we don’t see as real but great at generating images based on our perception of what we do see as real…it suggests that we might want to think about deepfake technology as not simply AI-powered but also human-powered…the idea here isn’t that AI is just better than humans but that it can do more with less—and by doing less it can produce more convincing results.”

They argue that deepfakes are not only being used online but can also be created offline using optical character recognition (OCR), computer vision algorithms, speech recognition technologies and other artificial intelligent capabilities.

In February 2019 Joint Warfighting Computational Intelligence Symposium writer Brian Blau wrote:

"Deepfake technology has been around long before artificial intelligence became popularized" "Although it was first publicly demonstrated in 2013 when researchers used DeepMind's AlphaGo Zero computer program for an initial experiment known as 4Chan DeepFake . . . This development was followed up by researchers from Google who developed Google TensorFlow , an open source machine learning framework that allows developers

5. Types of Deep Fake

Deepfake technology has rapidly gained in popularity over the past few years, with many being labeled as a “threat to freedom.”

The ability to create convincing fake content has received a lot of attention and debate in recent years, with some claiming that it is already available, and others arguing that it is still controversial.

In terms of benefits, deepfake technology offers many advantages over traditional methods of creating fake content. Such advantages include portability; most deepfakes are freely available online and can be downloaded quickly, making them easy for anyone to view.

Disadvantages include being able to continue existing traditional methods of producing fake content (such as video clips), making websites more vulnerable to hacking attacks and other forms of cybercrime, as well as requiring technical expertise from both the production side (creating the videos) and the viewing side (detecting what is real and what isn’t).

A variety of security measures are used by companies such as Google to combat these types of threats, including software-based antivirus systems designed specifically to detect deepfake technology.

Some governments have also passed legislation that bans deepfakes or at least requires companies who produce them to register them with at least one mandatory encryption system or else face criminal penalties. In recent years, however, it has also been argued that deepfakes should be classified as a type of art rather than a threat because they do not appear on computer screens and are not made in front of human observers.

6. Social Media and Deep Fakes

There are advantages and disadvantages to using deepfake technology on social media.

Advantages:

You can create an image and fake audio (the perfect recording of a voice or song) that sounds realistic but is not intended to be real. However, the images and audio can be manipulated by the user, so they may look like something other than what they were intended to look like.

In addition, you can use it as a form of extortion. If someone posts some content on social media without permission, you could claim that the content is fake and illegal and threaten legal action against them. This can help you gain attention from followers who might have otherwise been deterred from following your account because of legal concerns. Alternatively, if someone posts some content on social media, it could be fake in nature but not intended for public consumption. In this case, you could claim that the content is not fake but for personal use only (e.g., an image that one friend had taken), which would appear less threatening to your followers and cause them to share it more widely as well as generate revenue from ads associated with the already shared content (e.g., a link back to your profile).

Disadvantages:

If done correctly and properly posted so as not to violate copyright laws or other intellectual property rights, deepfakes are illegal under United States law, although most cases are resolved amicably with no criminal penalties attached or minor civil penalties waived (i.e., only fines will be levied). Nevertheless, if done incorrectly or improperly posted without proper consent/permission from its owner/owner’s representative (e.g., if one user posts a video on Instagram without asking permission before using it on their own account or posting it elsewhere), then by definition the video is false advertising because its creator cannot guarantee that there is no copyright infringement involved in posting it; however, most courts do not consider this type of false advertising in determining whether parties have violated another’s intellectual property rights [1]. There may also be good reasons why one wants others to falsely believe something about themselves; for example, if one wanted their employer/employees/customers/customer base to think good things about them [2]. One could also argue that deepfakes are not always produced with malicious intent; for example, questionable motives for posting misleading information about oneself are often found in advertisements depicting celebrities who have recently appeared in magazines where photographs are used without permission [3].

7. Warnings for Future Generations

Disruptive technologies are often called disruptive because they disrupt an industry, but also because a disruptive technology can have a positive impact on an individual’s life. In this article, we use the term deepfake to refer to an artificial intelligence technique for creating convincing pictures, audio, and video forgeries. Deepfake technology is currently the subject of hot debate due to its ability to mimic the appearance and features of real-world images. While more advanced techniques such as 3D printing may be able to produce similar results shortly, deepfake technology has the potential to provide more realistic recreations of real-world objects in a much shorter amount of time.

The term deepfake has been around since 2010 when researchers published research showing that deep learning algorithms could imitate human behavior. The initial research was conducted using images taken with a smartphone camera. The simulated videos were created based on real-life footage by training the algorithm from videos taken from YouTube and Vimeo. Since then, researchers have developed algorithms that make it possible for them to create realistic videos or fake images of objects even when those objects are not physically present in the footage. Over time, researchers have expanded these capabilities over at least two dimensions: depth (to recreate depth perception), and 3D imaging (to reproduce 3D geometry). As more powerful algorithms become available, humans will be able to construct believable simulations with greater accuracy than before. However, it may be some time before we can create realistic things like clothing and cars out of our simulated fake creations that would resemble their real-world counterparts (such as our current attempts at creating believable fake cars).

8. Conclusion

The technology of deepfake is still being experimented with, but it has been around since June 2015. It is in its early stages of development and has not been perfected yet.

In this article, I will be discussing the benefits and disadvantages of deepfake technology.

The technique was first introduced by researchers at the University of California, Berkeley in 2014. Since then, a variety of researchers across the globe have explored applications for deepfake technology. For example, a group from the University of Central Florida created a fake video to promote a study on people’s attitudes towards religion (see below). A group from the University of Technology Sydney created an emoji that could be used to represent various emotions (see below). A research team from South Korea created an emoji that could represent different emotions (see below). A group from Harvard created an emoji that can represent different emotions (see below). There are many more examples to list.

The technology has several advantages over conventional ways of creating fakes; first and foremost, it is cheaper than traditional methods. In addition, there are no guarantees that the footage will work in real-life conditions or will be accepted by viewers as authentic – both limitations that are present with conventional fakes in addition to its advantages over traditional fakes:

When using deepfake technology as a tool for fraud purposes, you can create convincing fakes with ease because it requires relatively little skill – you don’t need to know how to edit videos or anything else about video editing software; you only need some knowledge about basic computer skills and programming languages like C++ or Python. Moreover, you do not need special equipment or equipment that looks like professional cameras because deepfakes can be made through simple devices such as smartphones and tablets.

To sum up my views on this topic: Deepfake technology is still developing in terms of quality and usability but it has many advantages over other types of media fraud. It reduces costs significantly and doesn’t require much expertise to use it successfully – so, in my opinion, it should be considered more useful than conventional media fraud techniques. I hope this article has given you some more insight into this topic and helped you understand where we are headed concerning media fraud techniques.