What is deepfake and how does it work?
Although the term itself dates back to 2017, the technology behind deepfakes isn’t new at all. It can actually be traced to the 90s and is based on other technologies, such as:
- Artificial Neural Networks (ANNs)
- Artificial Intelligence
- Generative Adversarial Networks (GANs)
To simplify it, deepfakes, as we know them today, work by encoding a person into a latent space. In other words, they use an encoder to place an image or a video of a person over another person to manipulate visual and audio content.
This technology is so powerful yet so terrifying, and you’d be surprised to learn that it is actually legal.
And not only that, it has become so widespread and so accessible that anyone can generate some sort of a deepfake by using FaceSwap apps on their smartphones.
However, because of many incidents, including identity theft, fraud, and spreading misinformation, the use of this technology has been limited in many countries. Moreover, as the expansion of deepfakes grew, so did the development of deepfakes detectors.
To this day, a 100% accurate deepfake detector hasn’t been invented yet, although some companies are getting really close to it.
Important milestones in deepfake technology
Since the technology first emerged at the beginning of the 90s, we’ve seen how rapidly it progresses. These are the most important milestones that have marked the history of deepfakes:
- Video rewrite program (1997) – An example of face reanimation by manipulating existing footage of a person mouthing to a different audio track.
- Face2Face program (2016) – An example of re-enacting facial expressions in real-time by modifying video footage of someone’s face to depict them mimicking the facial expressions of someone else.
- Synthesizing Obama (2017) – An example of synthesizing mouth shapes from audio.
- Community r/deepfakes (2017) – Reddit users coined this term and shared deepfakes that they created.
- Fake dancing app (2018) – An example of how deepfakes don’t need to be focused only on the face but on the entire body.
- FakeApp (2018) – Desktop app available to users for free for swapping faces. Soon after its release, other variations appeared, such as Faceswap, DeepFaceLab, etc.
- DataGrip full body deepfake (2019) – This Japan-based company showed how deepfake could be used to create a person from scratch for commercial use.
- Impressions (2020) – The first mobile app designed for users to create deepfake video content of celebrities.
- America’s Got Talent (2022) – Deepfake technology was used to resurrect Elvis Presley on this talent show.
However, we need to mention advancements in deepfake detection technology.
Most detectors work by using algorithms to analyze pixels around the face frame and identify possible blurs or other inconsistencies, such as lighting reflections or irregular blinking.
On the other side, one innovative way of detecting deepfakes is by implementing blockchain technology to verify the source of the media. This way, only the videos verified as coming from trusted sources could be published online.
Ethical implications of deepfakes
When you look into harmful applications of deepfakes, you may be stunned by one fact. In 2019, AI firm Deeptrace analyzed around 15,000 deepfake videos and found that 96% of this material was in fact pornographic. Most of it included female celebrities’ faces, especially British and American actresses, as well as South Korean k-pop stars.
This stat shows that the main application of deepfakes has a deeply negative connotation. But that’s not the only bad example.
- Blackmail – Deepfakes have great potential for creating blackmail material, precisely because of their hyperrealistic effect. Moreover, with easily accessible software, it is possible to create vast amounts of blackmail content, hence the term “blackmail inflation”.
- DeepNude app – This app was available for Windows and Linux and was made with the purpose of removing clothing from photos of women. There was both a free and a paid version ($50). Luckily, soon after its launch, app developers withdrew it and refunded customers.
- Defamation – Besides celebrities, the main victims of defamation deepfakes are politicians, such as Joe Biden or Volodymyr Zelenskyy. Here we’re not talking about entertaining or funny videos that are obviously fake; we’re talking about manipulating the public and affecting someone’s public image on purpose.
- Fraud and scams – There have been examples where users would receive audio instruction from what they thought was a trusted individual. This mostly refers to businesses where employees or CEOs were asked to transfer funds into another account by the impersonated voice of someone they trust.
For example, from January 10, new regulations for deepfakes will take effect in China, aimed at content creators who use this type of technology. In the US, the Malicious Deep Fake Prohibition Act was introduced in 2018. In the UK, deepfake creators can be sued for identity theft and be prosecuted for harassment.
In conclusion, what’s important to emphasize is that deepfakes usually don’t have the power to disrupt whole government systems, but they have proved to be able to ruin individual lives and affect someone’s reputation long-term.