Deepfake technology and songs that singers sing after their death

Artificial intelligence sang Tamasha by Shadmehr with Mahsti’s voice! This news recently spread on social networks and surprised the fans of this late Iranian singer. The songs created with artificial intelligence were not limited to this Iranian song. Some time ago, a song with the voice of Kanye West, the American rapper, came out, after which he announced that he did not sing this song at all! But what is behind the scenes of these songs? The answer to this question is summarized in deepfake AI technology. Would you like to learn more about the details of this technology and see what Deepfake is and how it is used? Stay with us until the end of this article.

What is deepfake?

Deepfake artificial intelligence is a type of artificial intelligence that is used to create fake images as well as audio and visual deceptions. This term describes both the technology and the fake content that comes from it, and is an example of fake deep learning.

Deepfakes usually change existing source content and replace one person’s voice or image with another. This technology creates audio or video content that is completely identical to one’s original version; A deepfake shows someone doing something or saying something that person has never done or said.

The biggest danger that deepfake can pose is the dissemination of false information that appears to come from reliable sources and the audience has no doubt that it is fake. For example, in 2022 there was a deepfake video of Ukrainian President Volodymyr Zelensky calling for his soldiers to surrender.

Also, concerns have been raised about the possibility of interference in elections and electoral campaigns using deepfakes. Although deepfake technology poses serious threats to many people, especially celebrities, it also has legitimate uses, such as audio and video game entertainment, and customer support and caller-answering applications, such as call dispatch and reception services.

A brief history of deepfake technology

In 2017, a Reddit user with the username “deepfake” shared pornographic videos created using face-changing technology, in which he replaced the faces of celebrities with the original subjects.

Although deepfake technology has been used in various industries, to date it has been used more than any other industry in the pornography industry. A 2019 report by Amsterdam-based cybersecurity firm Sensity (formerly Deeptrace) found that “illegal deepfake pornography accounts for 96% of all deepfake videos online.”

But this is not where Deepfake’s story begins, ends, or does its best work. Deep learning technology—which includes early versions of the models that build deepfakes, also known as artificial media—has been around for decades, but the limited graphics processing power of computers in the past made it impossible to do many tasks.

According to freeCodeCamp contributor Nick McCullum, Geoffrey Hinton, a cognitive psychologist and computer scientist, made a significant contribution to the study of deep learning by introducing the artificial neural network.

Hinton’s artificial neural network, an integral component of today’s advanced deepfake techniques, was supposed to closely resemble the architecture of the human brain, passing signals through layers of nodes that process large amounts of data to learn and classify information.

Similar to the way neurons in the human brain make sense when processing their incoming data, ANNs pass raw data (noise) from input layers to their intermediate (hidden) layers and finally to the output layer.

As we’ll see when we get to the section on how to create a fake deep video, image, or audio using AI deep learning models, the most accurate artificial media outputs are those derived from large volumes of high-quality data.

For example, some of the most popular deepfakes are created by Chris Ume, a visual effects expert on the social network TikTok, which features amazing visual effects from Tom Cruise.

In an interview with The Guardian’s Science Weekly program, Ume explained that such complex deepfakes require “a lot of data – images, videos, whatever information you can find. “Then you sort and organize this data so that you only have the best data for your work.”

This abundance of available data is a big part of Ome’s work to make Tom Cruise’s videos look real and authentic. The Hollywood actor has been filmed and photographed for nearly 40 years, so the massive amount of data that can be used for machine learning (machine learning) turns the output of the work – the deepfake – into a stunningly accurate representation.

A key point in the history of deepfake is that deepfake technology, which is based on deep learning models, has been around for decades.

Deep learning has its roots in cognitive science and has been advanced over the years with the efforts of researchers in various fields including computer science, artificial intelligence, neurophysiology, cybernetics, and logic.

How does Deepfake work?

Deepfake uses two algorithms to create and modify fake content:

generator

discriminator

 The generator creates a training data set based on the desired output that creates the initial fake digital content, while the discriminator analyzes how real or fake the original version of the content is. This process is iterative, allowing the producer to become better at creating real content and the differentiator to become more adept at detecting defects to modify the producer.

A combination of generative and discriminative algorithms creates a Generative Adversarial Network or GAN for short. A GAN uses deep learning to recognize patterns in real images and then uses those patterns to create a fake image or video. When creating a depth image, a GAN system views the target images from different angles to capture all the details and perspectives. While creating a deepfake video, the GAN views the video from different angles and also analyzes behavior, movement, and speech patterns. This information is then run through the detector several times to adjust the realism of the final image or video.

Deepfake videos are created in one of two ways. They can use an original video source for the purpose, meaning the person is forced to say and do things they’ve never done before. Or they can swap a person’s face with someone else’s video, also known as face swapping or face swapping.

Below we introduce some specific approaches to create a deepfake:

Deepfakes with video source

When you use a video as a source to create a deepfake, a neural network-based deep auto-encoder analyzes the content to understand the relevant features of the target, such as facial expressions and body language. It then imposes these features on the original video. This autoencoder includes an encoder that encodes the relevant features. It also includes a decoder that imposes these features on the target video.

Deep voice fakes

For voice deepfakes, a GAN simulates a person’s voice, creates a model based on voice patterns, and uses that model to make the voice say whatever the creator wants. This technique is commonly used by video game developers.

  lip syncing

Lip syncing is another common method used in deepfake. Here, DeepFake gives the video a recorded voice and it sounds like the person in the video is speaking the recorded words. If the audio itself is a deepfake, then the video adds another layer of deception. This technique is supported by recurrent neural networks.

Technology required for deep fake development

Deepfake development is becoming easier, more accurate, and more common with the development and advancement of the following technologies:

GAN neural network technology is used in the development of all deepfake content, using generative and discriminating algorithms.

Convolutional neural networks, or CNNs, analyze patterns in visual data. CNNs are used for face recognition and motion tracking.

Autoencoders are a neural network technology that detects features related to a target, such as facial expressions and body movements, and then imposes these features on the source video.

Natural language processing or NLP is used to create the deepfake voice. NLP algorithms analyze target speech features and then generate original text using those features.

High-performance computing is a type of computing that provides the necessary computing power required by Deepfake.

According to the US Department of Homeland Security report on “The Growing Threat of Deepfake Identities,” several tools are commonly used to generate a deepfake within seconds. These tools include Deep Art Effects, Deepswap, Deep Video Portraits, FaceApp, FaceMagic, MyHeritage, Wav2Lip, Wombo and Zao.

What is the use of deepfake?

Deepfake can be used in any field. The main applications include the following:

Art: Deep fake is used to produce new music using original and existing elements of an artist’s work.

Blackmail and reputational damage: Examples of these are when a target image is placed in an illegal, inappropriate or otherwise inappropriate situation such as lying to the public, engaging in sexually explicit acts or using drugs. These videos are used to extort a victim, ruin a person’s reputation, take revenge or just cyberbullying. The most common blackmail or revenge use of deepfake is non-consensual fake deepfake, also known as revenge porn.

Caller Answering Services: These services use Deep fake to provide personalized responses to caller requests, including call forwarding and other reception services.

Telephone customer support: These services use fake voices for simple tasks like checking account balances or filing complaints.

Entertainment: Hollywood movies and video games simulate and manipulate actors’ voices for certain scenes. Entertainment media use this feature to create a scene that cannot be filmed, or to save time for the cast and crew. Deepfake is also used for satirical and parody content where the audience realizes the video is not real but enjoys the humorous situation the deepfake has created. For example, we can mention the 2023 Deepfake of Dwayne “The Rock” Johnson as Dora the Explorer.

Fake Evidence: This application of deepfaking involves creating false images or sounds that are intended to be used as evidence of guilt or innocence in a legal case.

Fraud and spoofing: Deep fake is used to impersonate an individual to obtain personally identifiable information (PII) such as bank account and credit card numbers. This fraud may sometimes involve impersonating company executives or other employees with an organization’s credentials to access sensitive information, which is a major cybersecurity threat.

Misinformation and political manipulation: Deep fake videos of politicians or trusted sources are used to influence public opinion. Sometimes this use of deep fake is referred to as spreading fake news.

Stock manipulation: Deep rigging is sometimes used to influence the stock price of companies. For example, a fake video of an executive making damaging statements about his company can send its stock price down. A fake video about a technological breakthrough or product launch can boost a company’s stock.

Text Messaging: The US Department of Homeland Security’s report titled “The Growing Threat of Deep Fake Identities” mentions text messages as a future use of deep fake technology. According to this report, fraudsters and threat actors can use deepfake techniques to replicate a user’s messaging style.

Deep fake detection methods

There are several top methods for detecting deepfake attacks. The following are signs of possible deepfake content:

unusual or inappropriate facial posture;

abnormal facial or body movement;

unnatural colors;

Videos that look unusual and strange when zoomed in or out;

discordant sounds;

People don’t blink.

In deepfake text, there are several indicators to identify:

presence of spelling mistakes;

Sentences that are not written naturally or their information is incorrect;

suspicious email address;

A phrase that does not match the tone and speech of the supposed sender;

Out-of-text messages that are not related to any discussion, event or topic.

However, with the advancement of artificial intelligence, some of these identification indicators have been removed. For example, artificial intelligence tools consistently outperform some of these indicators, such as tools that create natural blinking.

Noisy examples of deep fakes in the world

There are several notable examples of deepfakes, including the following:

Mark Zuckerberg, the founder of Facebook, was the victim of a deepfake that showed how Facebook acquired its users. The video was designed to show how social media platforms like Facebook can be used to trick people.

In 2020, US President Joe Biden was the victim of numerous deepfakes depicting him in exaggerated states of dementia and cognitive decline. The purpose of these deep fakes was to influence the outcome of the presidential election. Other US presidents such as Barack Obama and Donald Trump have also been victims of deepfake videos, some of which are meant to spread false information and some are for humor and entertainment.

During the 2022 Russia-Ukraine war, a video showed Ukrainian President Volodymyr Zelensky telling his troops to surrender to the Russians.

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