Blog 04 Jul 2023

How Biometric Liveness Detection Prevents Deepfakes?

Author: teresa_myers | 04 Jul 2023

How Biometric Liveness Detection Shields Businesses from Deepfakes

In today’s business landscape, the reliance on biometrics as a security method is growing significantly. The global biometrics market, valued at US$ 33.2 Billion in 2022, is projected to surge to an estimated $87.4 billion by 2028. This remarkable growth outlines the importance of liveness detection technology in ensuring secure business transactions.

However, as the usage of biometrics for authentication and verification increases, so does the sophistication of fraudulent activities. In 2021, The FBI sounded the alarm bells on the escalating menace of synthetic content, which encompasses deep fakes and manipulated digital media. This manipulation isn’t just limited to visuals but extends to audio as well.

In 2020, businesses reportedly lost around $250 million due to such sophisticated scams. A particularly alarming instance was that of a British energy company‘s CEO, who was hoodwinked by fraudsters wielding deep fake audio technology. This deception culminated in an erroneous transfer of $243,000.

In this article, we discuss the world of deepfakes, their origin, and the importance of biometric liveness detection as a defence against these emerging threats.

Deepfake: Meaning, Origin, and Proliferation

What Are Deepfakes?

A deepfake is a fake representation of an individual in an image or a video. It uses someone’s likeness to falsely portray that individual, without his consent or knowledge. The danger with deepfakes is that they look hyper-realistic, and sometimes it becomes tough for someone without technical knowledge to identify it as a fake. 

Deepfakes make use of Generative Adversarial Networks (GANs) to create synthetic media that is almost indistinguishable from real content. These manipulations are not limited to video, but they also include audio manipulation that is surprisingly accurate. This combination makes it very hard for anyone to detect it as a deepfake, and quite often it is passed on as an authentic video by fraudsters.

Origin of Deepfakes

The origin of the term “deepfake” is a fusion of “deep learning,” which is a subset of machine learning, and “fake.” Deep learning involves multiple layers of machine learning algorithms that extract high-level features from raw input data, making it capable of learning from unstructured data, such as human faces and movements. The complex machine learning algorithms can replicate features and eventually come up with very accurate representations.

How Deep Fakes Work?

Deepfake technology is powered by a specialised machine learning system consisting of two neural networks: a generator and a discriminator. 

These networks engage in a complex competition, with the generator’s role being to learn from a training dataset (e.g., facial photographs). It then generates new data (e.g., fake photographs) mimicking the same characteristics. Through iterative refinement, deepfakes progressively become more convincing, presenting a substantial threat to personal identity.

This technology has seen rapid growth, with a 300% increase in deepfake videos online in 2020. Deepfakes have been employed for political purposes, raising concerns about misinformation and widely used for blackmailing purposes.

The Threat Landscape: Deepfakes and Fraud

statistics related to impact of synthetic identity fraud

Deepfake Incidents and Their Impact

The impact of deepfakes is evident even in high-profile cases, like the one involving actor Jordan Peele. He used genuine footage of Barack Obama and combined it with his impersonation to draw attention to the deepfake videos and the potential threats they could pose. 

Political Manipulation

Deepfakes have also been employed for political purposes. In 2019, a deepfake video featuring Facebook CEO Mark Zuckerberg emerged, falsely portraying him as confessing to controlling user data. Such videos can undermine public trust and have far-reaching consequences.

Deepfakes for Blackmail and Potential Dangers

The adaptability of deepfake technology poses significant risks in terms of blackmail and personal deception. Here are some potential dangers:


Deepfake videos, often created by morphing faces into explicit content, have been used for blackmail. Victims, particularly women, have faced threats of exposing these manipulated videos, leading to emotional distress and coercion.

Financial Extortion 

Deepfake creators could target individuals or company executives by threatening to release damaging videos unless they pay a ransom. Such extortion attempts can have severe financial and reputational consequences.

Distinguishing Deepfakes from Other Fraudulent Activities

It’s crucial to differentiate deepfakes from other fraudulent activities

Presentation Attacks 

Presentation attacks involve using facial features, such as photos or videos, to impersonate someone during identity verification. 

Spoof Attacks

Spoof attacks typically involve using photos or videos of the target person to deceive facial recognition systems. While related to deepfakes, they rely on real images rather than entirely synthetic content.

Synthetic Fraud

Synthetic identity fraud involves creating entirely new, fictitious identities. Deepfakes can contribute to this by generating fake photos and videos of non-existent individuals.

Biometric Liveness Detection: The Solution

In the battle against deepfakes, Biometric Liveness Detection emerges as the most innovative countermeasure. This advanced technology offers a robust solution to verify the authenticity of individuals, ensuring security and trustworthiness in an increasingly digital world.

Biometrics Authentication

Biometrics, based on unique physical and behavioural attributes of individuals, holds the key to reliable authentication. Facial recognition and voice recognition are two prominent biometric modalities that, when combined, create a formidable defence against deep fakes.

Facial Recognition with Liveness Detection

Facial biometrics involve analysing distinct facial features, such as the arrangement of eyes, nose, and mouth. By comparing a live image of a person’s face with their stored biometric data, systems can determine if the person is genuine or an imposter.

Furthermore, advanced 3D liveness checks, leveraging 3D face mapping technology, confirm the physical presence and true authenticity of individuals, adding an extra layer of security to counter deepfake threats in live feeds or authentication procedures.

Voice Recognition

Voice biometrics assess vocal characteristics, including pitch, tone, and speech patterns. This technology confirms the authenticity of an individual’s voice, ensuring that they are indeed who they claim to be.

Video Injection Attack Detection

In the battle against deepfakes and synthetic identity fraud, Video Injection Attack Detection emerges as a critical and sophisticated component. Its role is paramount in safeguarding organizations and individuals from the escalating threats posed by these fraudulent activities.

Facia’s Video Injection Attack Detection

In the battle against deepfakes and synthetic identity fraud, Facia’s Video Injection Attack Detection employs advanced 3D liveness checks, powered by 3D face mapping technology, to enhance security. These checks confirm an individual’s physical presence and true authenticity, countering deepfake threats effectively.

Protection Against Man-in-the-Middle Attacks

In addition to verifying the video authenticity, Facia’s robust system provides a formidable defence against man-in-the-middle attacks. It accomplishes this by confirming that the user is interacting directly and naturally within the video, thereby preventing potential intermediaries from seeking to exploit vulnerabilities in the verification process. 

This defence mechanism safeguards the integrity of the identity verification process, ensuring that it remains secure and resistant to manipulation.

Final Thoughts

In a world where digital deceptions are becoming increasingly sophisticated, the need for robust defences against deepfakes and synthetic identity fraud is mandatory. Biometric liveness detection with its advanced technologies like 3D face mapping and liveness checks, emerges as a key defence mechanism

The combination of biometrics and Video Injection Attack Detection not only verifies authenticity but also ensures the physical presence of individuals during identity verification processes.

The rise of deepfakes and synthetic identity fraud may present significant challenges, but with innovative solutions like Facia, businesses can fortify their digital defences and protect what matters most.
Facia builds trust, strengthens security and Protects identities.


Frequently Asked Questions

Why deepfakes are security concerns?

Deepfake technology poses significant security concerns due to its ability to create fake identities and steal real ones. Attackers can forge documents and mimic voices, enabling them to impersonate individuals, potentially leading to fraudulent account creation and unauthorized transactions.

What are video injection attacks?

Video injection attacks involve fraudsters injecting fake videos into identity verification processes. They manipulate or spoof video streams, attempting to deceive verification systems. Video Injection Attack Detection technology plays a crucial role in detecting and preventing these fraudulent activities.

How does biometric liveness detection work?

Biometric liveness detection uses unique physical and behavioural traits, such as facial recognition, to verify an individual’s identity. It ensures that the person is genuinely present during verification, adding a crucial layer of security against deepfake and identity fraud.

What is 3D face mapping?

3D face mapping creates a detailed three-dimensional model of an individual’s face. This technology enhances security in biometrics by providing a highly accurate reference point for identity verification. It makes it more challenging for fraudsters to manipulate or impersonate facial features.

What are Man-in-the-Middle Attacks? 

Man-in-the-middle attacks in identity verification involve malicious intermediaries intercepting communication between the user and the verification system. They aim to manipulate or impersonate one of the parties involved, potentially leading to identity fraud or unauthorized access. Guarding against such attacks is crucial for security.