Have you seen the video of Mark Zuckerberg, the founder of Facebook, talking about having “total control of billions of people’s stolen data”? Or maybe some videos of Barack Obama, Donald Trump, and Joe Biden singing? If yes, then you have encountered deepfakes.
Deepfake technology emerges from the realm of Artificial Intelligence known as deep learning that is used to create convincing images, audio, or even videos of hoaxes and fabricated scenarios. There are two primary methods employed in the creation of deepfakes: transforming existing source content to substitute an individual with another individual, or creating an entirely original content depicting an individual engaged in certain actions or spouting utterances that they have never done or said before.
Deepfake technology generally employs two algorithms: a generator and a discriminator. The generator constructs a training data set and creates the initial deepfake content. Then, the discriminator assesses how realistic the generated deepfake content is. As both processes continue and keep repeating, the generator can enhance its ability to create more realistic content. At the same time, the discriminator can sharpen its proficiency in spotting flaws and prompting corrections for the generator.
The combination of the generator and discriminator algorithms form a generative adversarial network (GAN). In order to capture all the details, when creating a deepfake image, the GAN examines target images from diverse angles and perspectives. For deepfake videos, the GAN analyzes the behavior, movement, and speech patterns. And for deepfake audios, the GAN creates a customizable model based on a person’s voice and vocal patterns.
The accessibility of deepfake creation has raised concerns about the potential misuse associated with this technology. In the present day, nearly anyone can manipulate videos, audio, and images to alter their appearance without requiring advanced programming skills.
The simplicity of the process, taking mere seconds, underscores the need for heightened awareness and vigilance in combating the spread of deceptive content. With these user-friendly tools and cloud-based services, people can effortlessly produce deepfakes. It is also worth noting that while platforms such as Zao, My Heritage, d-id initially emerge for entertainment purposes, the ease of access to these applications poses significant challenges to the integrity of visual and auditory information.
In general, deepfakes can pose a huge range of threats, ranging from blackmail and character defamation to privacy breaches and manipulation. Even though early deepfakes were confined to pornography, focusing on swapping famous artists' faces into adult film actors/actresses, now the evolution of this technology has given rise to more alarming use cases. For example, fabricating alibis in courtrooms, committing fraud, extortion, and even acts of terrorism.
Within the context of eKYC specifically, deepfakes introduce a major threat by enabling the impersonation of individuals to gain illicit access to other people’s personally identifiable information (PII), including sensitive data like credit card numbers. Not stopping at gaining access, deepfakes can also be employed to utilize stolen identities to create new accounts. Spanning from something as simple as social media to something as complex as a bank account or a loan. As a result of identity theft, people who become victims may face unexpected fees, unauthorized spending, loans, and other negative outcomes. These consequences also go beyond just financial troubles and can cause additional problems like legal issues, being targeted by harmful organizations, and challenges in recovering their stolen identity.
The increasing ease with which false identities can be created using this technology has reached alarming levels, complicating efforts to track and verify the legitimacy of the content we see today.
Before, to counter deepfake threats, we used to simply deploy active liveness as a detection mechanism. It was once considered sufficient due to its ability to identify issues like unnatural movement or blinking in manipulated videos. However, the alarming evolution of deepfake technology has propelled these fabrications to a level of realism that now challenges the efficacy of active liveness. The need for a stronger implementation of active liveness has become apparent in the light of deepfakes’ capacity to produce these lifelike videos.
Interestingly, the solution to this escalating problem may involve leveraging AI against itself. While we do use AI as a tool to create deepfakes, we can also harness it for defensive purposes by training it on large datasets to discern several key indicators in deepfake contents. An innovative strategy involves the development of a deepfake detection API, finely tuned to identify various anomalies and notify the eKYC system. These anomalies may include unusual or awkward facial positioning, unnatural facial or body movements, aberrant coloring, videos exhibiting peculiarities upon zooming in, or inconsistencies in audio. By incorporating such nuanced features into eKYC systems, this AI-driven approach seeks to strengthen deepfake detection capabilities in eKYC, proactively reinforcing the integrity of identity verification systems in the digital age.
As incidents of identity forgery and spoof attacks continue to surge, staying a step ahead of fraudsters becomes increasingly crucial. Incorporating AI technologies into your electronic Know Your Customer (eKYC) systems emerges as a highly effective strategy in this ongoing battle against cyber threats. One particularly impactful implementation is the use of deepfake detection API to discern and detect identities generated by other artificial intelligence programs. This approach not only fortifies eKYC systems but also the whole area of identity verification.
