Although initially used for entertainment and parody, deepfakes have already made their way into advertising campaigns and have been used to increase awareness of the technology.
Snapchat, FaceApp, and similar apps have popularized face-swapping, which is the basis for deepfake technology.
This article will explore how deepfake technology works, its risks and dangers to society, and ways to detect deepfakes.
What are Deepfakes?
Deepfakes are manipulated images or videos created using machine learning (ML) and artificial intelligence (AI) algorithms to replace someone’s face or voice with another person’s, creating a new identity.
Basically, deepfake technology utilizes deep learning algorithms that can learn how to solve problems by analyzing large amounts of data to make alterations. The algorithms enable the creation of synthetic media that can mimic a person’s appearance and voice with remarkable accuracy.
One of the most common methods for creating deepfakes involves leveraging deep neural networks and autoencoders. These can examine a target video and a series of video clips featuring an individual. The result is a believable video that displays that person within the target video.
How are Deepfakes Used?
To produce a convincing deepfake video, two machine learning models are utilized: one generates fake videos from a dataset of sample videos, and the other identifies whether the video is real or fake.
Deepfakes are often enhanced by, Generative Adversarial Networks (GANs), another type of machine learning. The GAN technique trains these two models to compete against each other until the second model can no longer distinguish between real and fake videos. The outcome is a deepfake that appears realistic to human viewers.
GAN is most effective in generating realistic deepfakes when a vast dataset is accessible for it to learn from.
This is why celebrities and politicians are popular targets for deepfakes. This is because they have a significant public presence and provide abundant material for AI to capitalize on.
For instance, Chris Ume, a leading expert in this field, has created remarkably realistic deepfakes of Tom Cruise on TikTok, which have gone viral. According to Ume, creating sophisticated deepfakes requires a large amount of data, which must be meticulously cleaned to ensure only the best data is used.
Here are some of the most impressive deepfake examples that showcase the technology’s alarming potential and imaginative opportunities:
This Morgan Freeman deepfake is one of the most frighteningly realistic ones out there. The video was initially posted on the Dutch deepfake YouTube channel “Diep Nep” last year, and the concept was credited to Bob de Jong, with the impressive voice acting by Boet Schouwink.
The appearance of Luke Skywalker in The Mandalorian season two finale ignited the Star Wars fandom. However, viewers were quick to criticize the digital recreation of a younger Mark Hamill.
YouTuber Shamook later created a deepfake of Return of the Jedi-era Luke Skywalker with impressive results. Interestingly, it was revealed that Industrial Light and Magic, the renowned visual effects company behind Star Wars, had hired Shamook.
Impersonators who mimic the voice and gestures of the source have contributed to some of the most convincing deepfake examples, such as the video produced by BuzzFeed and comedian Jordan Peele.
Using After Effects CC and FakeApp, Peele’s mouth was superimposed over Obama’s, with his jawline following Peele’s mouth movements. The footage was then refined using FakeApp for over 50 hours of automatic processing.
With the growing number of selfies taken by people and rapid advancements in technology, it may soon be possible for anyone to be used as a source for deepfakes.
Connection Between Digital Twin and Deepfake Technology
Digital twins and deepfake technology may seem unrelated, but recent developments have revealed a connection between the two.
Digital twins are virtual replications of physical systems or objects that can simulate and optimize their real-world counterparts. In contrast, deepfake technology uses AI and ML algorithms to create fabricated media.
The connection between these two technologies lies in the potential for digital twins to be manipulated using deepfake technology.
As digital twins become more prevalent in various industries, such as manufacturing and healthcare, the risks associated with deepfakes become more significant.
For instance, deepfake technology can manipulate sensor data in a digital twin, leading to incorrect readings and potentially dangerous outcomes. Likewise, in the healthcare industry, deepfakes can exploit medical data in a patient’s digital twin, leading to incorrect diagnoses and treatments.
Moreover, the increasing use of deepfakes in political propaganda and disinformation campaigns highlights the potential danger of deepfake technology in manipulating digital twins for malicious purposes.
The Risks and Dangers of Deepfake
The rise of deepfake technology has brought significant risks and dangers to our digital landscape. Deepfakes can be used to:
- Spread disinformation and propaganda
- Manipulate political discourse
- Damage reputations.
Moreover, deepfakes can be used for malicious purposes like blackmail and fraud. For instance, a deepfake video can be used to impersonate someone and extort money or sensitive information.
The risks of deepfakes also extend to the realm of security and privacy. Specifically, facial liveness verification, a feature of facial recognition technology that relies on computer vision to verify the presence of a live user, is highly vulnerable to deepfake-based attacks.
Such attacks can compromise the security of user data and lead to significant security concerns for users and applications.
There are three major types of deepfakes implications:
1. Political Implications of Deepfakes
The potential of deepfakes in a political context could harm democratic processes or otherwise disrupt government policy and sow discord among citizens.
The ability to create convincing fake videos of political leaders can lead to confusion, disinformation, and a loss of trust in public institutions. In addition, such forms of disinformation could manipulate and distort the complicated media ecosystem.
While legislation to ban political deepfakes would require significant carve-outs to protect freedom of expression, social media platforms can still limit or prohibit their use.
Fortunately, workable solutions are emerging from academic researchers and established technology companies such as Adobe and Microsoft.
The Coalition for Content Provenance and Authenticity created open, royalty-free technical standards to combat disinformation.
However, it is essential to have digital media literacy because deepfakes can be challenging to identify. It is necessary to remain vigilant about verifying the authenticity of online media.
2. Social Implications of Deepfakes
Social media platforms’ domination of our online lives has created an ever-present danger of deepfake content spreading unchecked.
Unfortunately, the creators of deepfakes have used this technology to exploit and harm people. For example, cybercriminals use deepfakes to commit identity theft and online fraud, while individuals fall victim to deepfake-enabled scams.
Furthermore, using deepfakes to create fake adult videos and images of public figures and celebrities can tarnish their reputation and dignity.
Academics are split on the impact of deepfake technology. Some believe deepfakes can potentially sway public opinion and change voting habits. Others view it as no more disruptive than other forms of misinformation online.
However, all agree that deepfakes contribute to the “liar’s dividend” problem and sow uncertainty, reducing trust in online news.
Technology companies such as Facebook and Google are combating the spread of deepfakes by detecting and removing manipulated content using advanced technology.
Policymakers have also enacted legislation to provide legal recourse for victims of deepfake pornography.
While promising, the solutions offered may not be effective due to the fast-changing environment of social media and the challenges associated with regulation.
Therefore, further quantification of the impact of deepfake technology on society is required to address the issue adequately.
3. Economic Implications of Deepfakes
In today’s information-based economy, deepfakes can cause severe damage to businesses and economies.
One of the most significant economic impacts is the potential for market manipulation. Deepfakes can create false or misleading information about a company, leading to changes in stock prices and decisions that benefit the creators of the deepfakes.
Furthermore, they can manipulate financial data, making incorrect predictions and investment decisions. This can ripple effect throughout the economy, as inaccurate financial data can lead to wrong assessments of market trends and risks.
Additionally, deepfakes can harm the reputation of businesses and individuals, resulting in lost revenue and opportunities. The spread of deepfake videos and images can cause negative publicity, mistrust, and loss of credibility, which can be challenging to recover from.
As a result, businesses and governments are exploring ways to prevent their malicious use.
One approach involves using AI and ML algorithms to detect and combat deepfakes, while blockchain technology can provide a secure and immutable data record to prevent its manipulation.
How to Spot a Deepfake
Detecting a deepfake can be challenging, but there are several methods that individuals can use to identify fake media.
Here are some of the top methods to identify a deepfake:
- Look for inconsistencies: Deepfakes often have inconsistencies in lighting, shadows, and reflections that are not present in real media.
- Analyze the audio: Pay attention to the audio in the video. Deepfakes may have unnatural or inconsistent audio quality, such as variations in background noise or voice pitch.
- Check for unnatural movements: Deepfakes may have artificial movements that are not typical of real people. Look for glitches or distortions in the video that do not match natural movement patterns.
- Compare with known sources: Deepfakes are often created by merging two or more existing videos or images. By comparing the deepfake with known sources, you can identify discrepancies.
- Use deepfake detection tools: Several tools have been developed to help identify deepfakes. These tools analyze various aspects of the media, such as facial expressions, eye movements, and skin textures, to detect if the media is manipulated.
For example, Microsoft’s Video Authenticator detects blending boundaries and grayscale elements invisible to the human eye, while Facebook’s Reverse Engineering detects fingerprints left by an AI model.
While these methods can help detect deepfakes, it is essential to remember that deepfake technology is advancing rapidly. New techniques are continually being developed to make them more realistic.
Given the rapid advancements in deepfake technology, it is essential to maintain a high level of vigilance and skepticism toward media, particularly when it appears too good to be true.
Benefits of Deepfake Technology
Deepfake technology, despite its controversial nature, has various benefits for businesses. This AI-based technology is now being used to reshape various industries, from marketing to education and entertainment.
Here are some of the positive applications of deepfakes technology:
1. Low-Cost Video Campaigns
With deepfake technology, marketers can create video campaigns without needing an in-person actor. Instead, they can purchase an actor’s identity license and use previous digital recordings of the actor to create a new video.
This can save time and money and also allow for easy edits to be made without the need for reshooting.
Deepfake technology allows brands to provide customers with more personalized messaging and experiences based on their preferences.
For instance, a brand can alter a model’s skin tone in their marketing to better suit a customer’s ethnicity or skin color, thus increasing inclusivity and reaching a broader market with their campaigns.
3. Bringing the Deceased Back to Life
Deepfake technology has also created interactive images of deceased artists and celebrities, enabling audiences to engage with them long after passing.
Examples include the Florida Dali Museum’s collaboration with advertising company Goodby, Silverstein & Partners to recreate a digital reanimation of Salvador Dali and Snoop Dogg’s music video featuring Tupac.
4. Saving Time and Labor in the Film Industry
Deepfake technology has the potential to save time and labor in the film industry by automating the face-swapping process currently done manually by VFX artists. This allows for more efficient and cost-effective production of films and other media.
5. Educating People in a More Interactive Way
Deepfake technology also makes online learning more interactive by generating lecture videos from text-based content or audio narration. Additionally, it can be used to construct artificial voices from historical figures, allowing them to tell their stories/speeches with their own voice.
6. Engaging with Viewers or Customers
Deepfake technology can also engage viewers or customers by providing personalized recommendations and offers to meet their needs.
For example, Reuters has created an AI-generated deepfake person responsible for presenting the sports news summary. In addition, fashion companies are using virtual fitting rooms where customers can deepfake their faces onto virtual models to see how clothes would look on them.
Wrapping Up On Deepfake Technology
Deepfake technology has become increasingly prevalent in today’s society. But, its uses and consequences are still shrouded in controversy.
Primary concerns surrounding deepfakes include:
- Political implications,
- Social implications,
- Economic implications.
Despite the efforts of technology companies and policymakers to combat the spread of deepfakes, the technological arms race between deepfake creators and deepfake detectors continues.
Therefore, it is necessary to remain vigilant and skeptical of media, particularly if it appears too good to be true. Although legislation can provide legal recourse for deepfake victims, it may not effectively combat the ever-evolving advancements in deepfake technology.
As technology advances, it is crucial to keep developing more effective methods of detecting deepfakes. These improved methods are necessary to identify and prevent the harmful effects of deepfakes from persisting.
This means that any legislation aimed at addressing the issue may only have the necessary impact once we have better ways of detecting deepfakes.