What is deepfake technology?

How do deepfakes work?

Deepfakes aren't edited or photoshopped videos or images. In fact, they're created using specialized algorithms that blend existing and new footage. For example, subtle facial features of people in images are analyzed through machine learning (ML) to manipulate them within the context of other videos.

Deepfakes uses two algorithms -- a generator and a discriminator -- to create and refine fake content. The generator builds a training data set based on the desired output, creating the initial fake digital content, while the discriminator analyzes how realistic or fake the initial version of the content is. This process is repeated, enabling the generator to improve at creating realistic content and the discriminator to become more skilled at spotting flaws for the generator to correct.

The combination of the generator and discriminator algorithms creates a generative adversarial network. A GAN uses deep learning to recognize patterns in real images and then uses those patterns to create the fakes. When creating a deepfake photograph, a GAN system views photographs of the target from an array of angles to capture all the details and perspectives. When creating a deepfake video, the GAN views the video from various angles and analyzes behavior, movement and speech patterns. This information is then run through the discriminator multiple times to fine-tune the realism of the final image or video.

Deepfake videos are created in one of two ways. They can use an original video source of the target, where the person is made to say and do things they never did; or they can swap the person's face onto a video of another individual, also known as a face swap.

The following are some specific approaches to creating deepfakes:

• Source video deepfakes. When working from a source video, a neural network-based deepfake autoencoder analyzes the content to understand relevant attributes of the target, such as facial expressions and body language. It then imposes these characteristics onto the original video. This autoencoder includes an encoder, which encodes the relevant attributes and a decoder, which imposes these attributes onto the target video.
• Audio deepfakes. For audio deepfakes, a GAN clones the audio of a person's voice, creates a model based on the vocal patterns and uses that AI model to make the voice say anything the creator wants. Video game developers commonly use this technique.
• Lip syncing. Lip syncing is another common technique used in deepfakes. Here, the deepfake maps a voice recording to the video, making it appear as though the person in the video is speaking the words in the recording. If the audio itself is a deepfake, then the video adds an extra layer of deception. This technique is supported by recurrent neural networks.

Technology required to develop deepfakes

The development of deepfakes is becoming easier, more accurate and more prevalent as the following technologies are developed and enhanced:

• GAN neural network technology uses generator and discriminator algorithms to develop all deepfake content.
• Convolutional neural networks analyze patterns in visual data. CNNs are used for facial recognition and movement tracking.
• Autoencoders are a neural network technology that identifies the relevant attributes of a target such as facial expressions and body movements, and then imposes these attributes onto the source video.
• Natural language processing is used to create deepfake audio. NLP algorithms analyze the attributes of a target's speech and then generate original text using those attributes.
• High-performance computing is a type of computing that provides the significant necessary computing power deepfakes require.
• Video editing software isn't always AI-based, but it frequently integrates AI technologies to refine outputs and make adjustments that improve realism.

According to the U.S Department of Homeland Security's "Increasing Threat of Deepfake Identities" report, several AI tools are commonly used to generate deepfakes in a matter of seconds. Those tools include Deep Art Effects, Deepswap, Deep Video Portraits, FaceApp, FaceMagic, MyHeritage, Wav2Lip, Wombo and Zao.

The Gyan Management Journal also highlighted several deepfake applications, such as Datagrid, which enables the creation of full-body personas from scratch, and Impressions, a desktop app designed for creating celebrity videos using mobile phones.

How are deepfakes commonly used?

The use of deepfakes varies significantly. The positive and negative uses of deep fakes include the following:

• Art. Deepfakes are used to generate new music using the existing bodies of an artist's work.
• Blackmail and reputation harm. Examples of this are when a target image is put in an illegal, inappropriate or otherwise compromising situation such as lying to the public, engaging in explicit sexual acts or taking drugs. These videos are used to extort a victim, ruin a person's reputation, get revenge or simply cyberbully them. The most common blackmail or revenge use is nonconsensual deepfake porn, also known as revenge porn.
• Caller response services. These services use deepfakes to provide personalized responses to caller requests that involve call forwarding and other receptionist services.
• Customer phone support. These services use fake voices for simple tasks such as checking an account balance or filing a complaint.
• Entertainment. Hollywood movies and video games clone and manipulate actors' voices for certain scenes. Entertainment mediums use this when a scene is hard to shoot, in post-production when an actor is no longer on set to record their voice, or to save the actor and the production team time. Deepfakes are also used for satire and parody content in which the audience understands the video isn't real but enjoys the humorous situation the deepfake creates. An example is the 2023 deepfake of Dwayne "The Rock" Johnson as Dora the Explorer.
• False evidence. This involves the fabrication of false images or audio that can be used as evidence implying guilt or innocence in a legal case.
• Low-cost video campaigns. Marketers using deepfakes can cut video campaign costs by licensing an actor's likeness and using existing digital recordings along with script dialogue to create new content without needing in-person actors.
• Fraud. Deepfakes are used to impersonate an individual to obtain personally identifiable information, such as bank accounts and credit card numbers. This can sometimes include impersonating executives of companies or other employees with credentials to access sensitive information, which is a major cybersecurity threat.
• Hyperpersonalization and inclusivity. Deepfake technology enhances brand personalization by adjusting elements such as ethnicity and skin tone to better reflect diverse customer demographics. This is used to foster inclusivity and broaden campaign reach.
• Misinformation and political manipulation. Deepfake videos of politicians or trusted sources are used to sway public opinion and, in the case of the deepfake of Ukrainian President Volodymyr Zelenskyy, create confusion in warfare. This is sometimes referred to as spreading fake news.
• Stock manipulation. Forged deepfake materials are used to affect a company's stock price. For instance, a fake video of a chief executive officer making damaging statements about their company could lower its stock price. A fake video about a technological breakthrough or product launch could raise a company's stock.
• Texting. The U.S. Department of Homeland Security's "Increasing Threat of Deepfake Identities" report cited text messaging as a future use of deepfake technology. Threat actors could use deepfake techniques to replicate a user's texting style, according to the report.
• Education. Education platforms are also using deepfake technology to develop AI tutors offering personalized support to students. For example, Claude, an educational AI assistant from Anthropic, answers students' queries, clarifies concepts and identifies gaps in understanding.
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Are deepfakes legal?

Deepfakes are generally legal, and there's little law enforcement can do about them, despite the serious threats they pose. Deepfakes are only illegal if they violate existing laws such as child pornography, defamation or hate speech.

At least 40 states have pending legislation aimed at the use of deepfakes. According to a Bloomberg Law article, five states have banned deepfakes that aim to influence elections and seven states are considering legislation in 2024 imposing a ban. At least 10 states have legislation in place making nonconsensual deepfake porn illegal.

The lack of laws against deepfakes is because most people are unaware of the technology, its uses and dangers. Because of this, victims don't get protection under the law in most cases of deepfakes.

However, certain recent legislative efforts have been in the making that if passed would consider extremely malicious deepfakes to be illegal and warrant legal actions against them. These include the following notable legislative actions:

• DEFIANCE Act. The Disrupt Explicit Forged Images and Non-Consensual Edits (DEFIANCE) Act, if passed, would become the first federal law to protect victims of deepfakes. It enables victims to sue the deepfake creators if they knew and recklessly disregarded that the victim didn't consent to its making.
• Preventing Deepfakes of Intimate Images Act. In May 2023, Congressman Joe Morelle introduced the Preventing Deepfakes of Intimate Images Act. This bill aims to criminalize the non-consensual sharing of deepfakes related to intimate images and the legislation seeks to protect individuals from the unauthorized creation and distribution of digitally manipulated intimate images.
• Take It Down Act. The Tools to Address Known Exploitation by Immobilizing Technological Deepfakes on Websites and Networks (Take It Down Act), sponsored by Sen. Ted Cruz, would criminalize publishing or threatening to publish revenge porn. It also requires social media platforms to develop a process for removing the faked images within 48 hours of receiving a valid request from a victim.
• Deepfakes Accountability Act. Congresswoman Yvette Clarke and Congressman Glenn Ivey introduced in September 2023 the Deepfakes Accountability Act, which requires creators to digitally watermark deepfake content and makes it a crime to fail to identify malicious deepfakes, including those depicting sexual content, criminal conduct, incitement of violence and foreign interference in an election.

How are deepfakes dangerous?

Deepfakes pose significant dangers despite being largely legal, including the following:

• Blackmail and reputational harm that put targets in legally compromising situations.
• Political misinformation such as nation states' threat actors using it for nefarious purposes.
• Election interference, such as creating fake videos of candidates.
• Stock manipulation where fake content is created to influence stock prices.
• Fraud where an individual is impersonated to steal financial account and other PII.

• Deepfake technology can fuel unethical actions such as creating revenge porn, where women are disproportionately harmed.

• Raising awareness and educating people about deepfakes could erode trust in genuine videos, causing an intellectual crisis in video evidence.
• Deepfakes can be used to deceive security measures or gain unauthorized access to systems. For instance, a deepfake could potentially bypass facial recognition systems used for authentication or access control.

Methods to detect deepfakes

There are several best practices for detecting deepfake attacks. The following are signs of possible deepfake content:

• Unusual or awkward facial positioning.
• Unnatural facial or body movement.
• Unnatural coloring.
• Videos that look odd when zoomed in or magnified.
• Inconsistent audio.
• People who don't blink.
• Tiny deviations in the reflected light in the eyes of the subject.
• The aging of the skin doesn't match the aging of the hair and eyes.
• Glasses either have no glare or have too much and the glare angle stays the same despite the person's movements.

In textual deepfakes, there are a few indicators:

• Misspellings.
• Sentences that don't flow naturally.
• Suspicious source email addresses.
• Phrasing that doesn't match the supposed sender.
• Out-of-context messages that aren't relevant to any discussion, event or issue.

However, AI is steadily overcoming some of these indicators, such as with tools that support natural blinking and other biometric evidence.

How to defend against deepfakes

Companies, organizations and government agencies, such as the U.S. Department of Defense's Defense Advanced Research Projects Agency, are developing technology to identify and block deepfakes. Some social media companies use blockchain technology to verify the source of videos and images before allowing them onto their platforms. This way, trusted sources are established and fakes are prevented. Along these lines, Meta and X, formerly known as Twitter, have both banned malicious deepfakes.

Many organizations offer deepfake protection software, including the following companies:Top of Form

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• Adobe provides a system that lets creators attach a signature to videos and photos with details about their creation.
• Intel FakeCatcher prioritizes speed and efficiency by analyzing subtle physiological details such as pixel variations in blood flow to achieve high accuracy in real-time detection.
• Microsoft offers AI-powered deepfake detection software that analyzes videos and photos to provide a confidence score that shows whether the media has been manipulated.
• Operation Minerva uses catalogs of previously discovered deepfakes to tell if a new video is simply a modification of an existing fake that has been discovered and given a digital fingerprint.
• Sensity AI offers a detection platform that uses deep learning to spot indications of synthetic media in the same way antimalware tools look for virus and malware signatures. Users are alerted via email when they view a deepfake.
• Sentinel is a cloud-based option that offers real-time deepfake detection by using various technologies including temporal consistency checks, facial landmark analysis and flicker detection to gauge manipulated media.

Notable examples of deepfakes

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

• Facebook founder Mark Zuckerberg was the victim of a deepfake in 2019 that showed him boasting about how Facebook "owns" its users. The video was designed to show how people can use social media platforms such as Facebook to deceive the public.
• Concerns were raised back in 2020 over the potential to meddle in elections and election propaganda. U.S. President Joe Biden was the victim of numerous deepfakes showing him in exaggerated states of cognitive decline meant to influence the presidential election.
• Presidents Barack Obama and Donald Trump have also been victims of deepfake videos, some to spread disinformation and some as satire and entertainment. During the Russian invasion of Ukraine in 2022, a video of Ukrainian President Volodymyr Zelenskyy was portrayed telling his troops to surrender to the Russians.

• In early 2024, authorities in Hong Kong claimed that a finance employee of a multinational organization was tricked into handing over $25 million to con artists posing as the business's chief financial officer over video conference calls, using deepfake technology. According to the police, the employee was duped into entering a video call with numerous other employees, but they were all deepfake impersonations.
• There's a TikTok account dedicated entirely to Tom Cruise deepfakes. While there's still a hint of the uncanny valley about @deeptomcruise's videos, his mastery of the actor's voice and mannerisms, along with the use of rapidly advancing technology, has resulted in some of the most convincing deepfake examples.

History of deepfake AI technology

Deepfake AI is a relatively new technology, with origins in the manipulation of photos through programs such as Adobe Photoshop. However, the development of deepfake technology can be traced back to the 1990s when researchers at academic institutions started exploring the use of AI for image processing.

By the mid-2010s, cheap computing power, large data sets, AI and machine learning technology all combined to improve the sophistication of deep learning algorithms.

In 2014, GAN, the technology at the heart of deepfakes, was developed by University of Montreal researcher Ian Goodfellow. In 2017, an anonymous Reddit user named "deepfakes" began releasing deepfake videos of celebrities, as well as a GAN tool that let users swap faces in videos. These went viral on the internet and in social media.

The sudden popularity of deepfake content led tech companies such as Facebook, Google and Microsoft to invest in developing tools to detect deepfakes. Despite the efforts of tech companies and governments to combat deepfakes and take on the deepfake detection challenge, the technology continues to advance and produce AI-generated images and videos that are increasingly convincing.

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