The use of generative AI to create deepfakes has stirred both fascination and fear. While initially used innocuously in movies like The Fast and the Furious, the malicious potential of deepfakes became evident with instances like the fake video of Hillary Clinton endorsing a political opponent. This raises concerns about the impact of deepfakes on public discourse and democratic processes.
The Response: In response to the growing threat of deepfakes, researchers have been actively developing countermeasures. Tech giants like Microsoft have joined the fight, aiming to mitigate the impact of deepfakes, especially in critical events like the 2024 U.S. presidential election. Efforts include highlighting a video’s provenance to help viewers discern authenticity. Despite these efforts, challenges persist, as seen in recent incidents like AI-generated robocalls spreading misinformation.
Global Concerns: The proliferation of deepfake technology has sparked global concern, particularly regarding its potential to undermine democratic processes. The looming national elections in 2024 have intensified these worries, leading to concerted efforts by companies and industry groups to safeguard against deepfake manipulation.
Understanding Deepfakes: Deepfakes represent a significant technological advancement, enabling the creation of convincing fake videos and images. They have been used maliciously, such as in non-consensual pornography involving celebrities and fabricated political statements. This has prompted the implementation of new laws and regulations to curb their dissemination.
Combatting the Threat: Social media platforms like Facebook and Twitter have taken steps to ban deepfakes, reflecting a broader effort to combat their spread. Meanwhile, conferences focused on computer vision and graphics feature discussions on innovative defense strategies against deepfakes.
What is a deepfake?
A deepfake is a type of technology that can seamlessly insert a person into a video or photo in which they never actually appeared. This capability has been around for decades, with one famous example being the resurrection of the late actor Paul Walker for the movie Fast & Furious 7. In the past, creating such effects required entire studios and years of expertise. However, with the advent of new automatic computer-graphics and machine-learning systems, deepfake technologies can now generate images and videos much more quickly.
Despite its widespread use, there is considerable confusion surrounding the term “deepfake.” Computer vision and graphics researchers often dislike the term because it has become a catchall phrase that encompasses a wide range of techniques, from advanced AI-generated videos to any image that appears suspiciously altered.
It’s important to note that not everything labeled as a deepfake actually fits the definition. For instance, a controversial video from the U.S. Democratic primary debate, known as the “crickets” video, was created using standard video editing techniques and not deepfake technology. This highlights the need for clarity and accuracy when discussing deepfakes and related technologies.
How deepfakes are created
The primary ingredient in deepfakes is machine learning, which has revolutionized the process, making it faster and more cost-effective. To create a deepfake video of someone, a creator begins by training a neural network on extensive real video footage of the individual. This training helps the network develop a realistic understanding of the person’s appearance from various angles and under different lighting conditions. Once trained, the network is combined with computer graphics techniques to overlay the person onto a different actor or scenario.
While AI has accelerated the process, creating believable deepfakes still requires time and manual adjustments to avoid noticeable flaws in the final image. Despite the widespread belief that generative adversarial networks (GANs) are the primary tool for deepfake creation, their role is somewhat overstated. While GAN-generated faces are incredibly realistic, they are challenging to work with and not well-suited for video synthesis. GANs struggle to maintain temporal consistency across frames, making them less suitable for video deepfakes.
In reality, most deepfake videos today are created using a combination of AI and non-AI algorithms, rather than relying solely on GANs. For example, even the well-known audio deepfakes, such as those produced by Canadian AI company Dessa (now owned by Square) using Joe Rogan’s voice, do not utilize GANs. Instead, a variety of AI and non-AI techniques are employed to produce convincing deepfakes.
Who created deepfakes?
Impressive deepfake examples often originate from university labs and the startups they inspire. For instance, a notable video featuring soccer star David Beckham fluently speaking nine languages, despite only knowing one, was developed using code from the Technical University of Munich, Germany. Similarly, MIT researchers crafted a remarkable video of former U.S. President Richard Nixon delivering an alternate speech prepared for the nation in case of Apollo 11’s failure. However, these advanced deepfakes aren’t the primary concern for governments and academics. Deepfakes don’t require sophisticated technology to wreak havoc on society, as illustrated by nonconsensual pornographic deepfakes and other problematic forms.
The term “deepfake” originates from a pivotal moment in 2017 when a Reddit user known as r/deepfakes utilized Google’s open-source deep-learning library to swap faces of porn performers with those of actresses. Today, the DIY deepfakes found online mostly stem from this original code. While some may be viewed as entertaining experiments, none can be considered convincingly authentic.
Why the concern? “Technology always improves. That’s just how it works,” explains Hany Farid, a digital forensics expert at the University of California, Berkeley. There’s no consensus on when DIY techniques will become refined enough to pose a genuine threat—predictions range from 2 to 10 years. Nonetheless, experts agree that eventually, anyone will be capable of producing realistic deepfakes using just a smartphone app.
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What are deepfakes used for?
The most pressing threat posed by deepfakes today is nonconsensual pornography, which makes up 96 percent of the deepfakes found online. While many of these target celebrities, there’s a growing trend of using deepfakes to create fake revenge porn, according to Henry Ajder, head of research at the detection firm Deeptrace in Amsterdam.
However, women aren’t the only ones at risk. Deepfakes could facilitate bullying in various settings like schools or workplaces, as they allow anyone to put individuals in absurd, dangerous, or compromising situations.
Businesses are concerned about the potential for deepfakes to amplify scams. There are unconfirmed reports of deepfake audio being used in CEO scams, where employees are tricked into sending money to fraudsters. Extortion could also become a significant issue. Identity fraud is a top concern regarding deepfakes, with over three-quarters of respondents in a cybersecurity industry poll by the biometric firm iProov fearing that deepfakes could be used for fraudulent online payments and hacking personal banking services.
Governments are particularly worried that deepfakes could undermine democracy. If you can make a female celebrity appear in a pornographic video, you could do the same to a politician seeking reelection. For example, in 2018, a video emerged allegedly showing João Doria, the governor of São Paulo, Brazil, who is married, participating in an orgy. Doria claimed it was a deepfake. Similarly, in 2018, the president of Gabon, Ali Bongo, who was long presumed unwell, appeared in a suspicious video to reassure the population, sparking an attempted coup.
The uncertainty surrounding these unconfirmed cases highlights the most significant danger of deepfakes: the liar’s dividend. This term refers to the cover deepfakes provide for individuals to deny any evidence of wrongdoing, dismissing it as a deepfake. Essentially, it’s a one-size-fits-all plausible deniability tactic. “That is something you are absolutely starting to see: that liar’s dividend being used as a way to get out of trouble,” explains Farid.
How do we stop malicious deepfakes?
To combat malicious deepfakes, several U.S. laws have been enacted over the past year. States like Texas, Virginia, and California have passed bills to criminalize deepfake pornography and prevent the use of deepfakes in elections. Additionally, in December, the president signed the first federal law against deepfakes as part of the National Defense Authorization Act. However, these laws only apply when the perpetrator resides in jurisdictions covered by them.
Internationally, only China and South Korea have taken specific actions to prohibit deepfake deception. In the UK, laws regarding revenge porn are under review by the law commission to address various methods of creating deepfakes. However, the European Union doesn’t view this issue as an immediate concern compared to other forms of online misinformation.
While research labs have developed methods to identify and detect manipulated videos, such as incorporating watermarks or blockchain technology, creating foolproof deepfake detectors remains challenging. Despite this, tech companies are making efforts to combat deepfakes. Facebook has enlisted researchers to build a deepfake detector and enforce its ban, while Twitter plans to tag deepfakes not removed outright. YouTube has also reiterated its stance against deepfake videos related to elections and voting procedures.
However, the challenge remains for deepfakes outside these platforms. Programs like Reality Defender and Deeptrace aim to protect users from deepfakes. Deeptrace utilizes an API to pre-screen incoming media, diverting obvious manipulations similar to how Gmail filters spam. Reality Defender, developed by AI Foundation, seeks to tag and flag manipulated images and videos before they cause harm. According to Adjer, from AI Foundation, relying on individuals to authenticate media is unfair, hence the need for such tools.
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