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Biometric Liveness Detection: Shielding Businesses from Deepfake Fraud in ID Verification

Author: teresa_myers | 04 Jul 2023

How Biometric Liveness Detection Prevents Deepfakes?

In 2024, a company fell victim to a sophisticated deepfake scam, losing a staggering $25 million through impersonation during a video call. This incident underscores the growing threat deepfakes pose to businesses relying on biometric authentication.

Deepfakes are a hyper-realistic form of synthetic media used to create convincing fake videos and audio of real people. This poses a significant danger to businesses as they rely on biometric authentication for secure ID verification (projected to be an $87.4 billion market by 2028), and deepfakes pose a significant security risk.

Key Takeaways

  • Deepfakes are hyper-realistic manipulated videos and audio that can be used for fraud.
  • Deepfakes are used for financial extortion, blackmail, and impersonation, making the deployment of effective facial liveness detection mechanisms critical.
  • Facial recognition and liveness checks, including 3D face mapping, offer a powerful defence against deepfakes.
  • Video Injection Attack Detection is crucial in safeguarding against manipulated video streams and man-in-the-middle attacks.
  • Facia provides advanced biometric liveness detection technology, known for its speed and accuracy (0% FAR at less than 1% FRR).

However, there is still a solution: 3d liveness detection. This advanced technology verifies a user’s physical presence during authentication, ensuring you’re not dealing with a manipulated video or recording.

In this blog, we’ll discuss the world of deepfakes, explore their impact on businesses, and unveil how biometric liveness detection acts as an impenetrable shield. We’ll also discuss the latest advancements in liveness detection technology, empowering you to safeguard your business from these sophisticated scams.

Deepfake: Meaning, Origin, and Proliferation

What Are Deepfakes?

Deepfakes are synthetic media that use artificial intelligence (AI) to create hyper-realistic fake videos or images of real people. These manipulated representations can be compelling, often portraying individuals saying or doing things they never did.

Origin of Deepfakes

The term “deepfake” is a combination of “deep learning” (a subset of AI) and “fake.” Deep learning algorithms are trained on massive datasets of real images and videos. These algorithms can then generate entirely new, synthetic media that closely resembles the training data, necessitating the deployment of robust presentation attack detection systems.

How DeepFakes Works?

Deepfake technology itself uses a specialized machine-learning system with two key components:

  • Generator Network: This network analyzes the training data (e.g., facial photographs of a person) and learns to identify key features. It then uses this knowledge to generate entirely new, synthetic data (e.g., fake videos or images) that mimic the target person’s appearance and mannerisms, highlighting the importance of machine learning in the prevention of deepfake attacks.
  • Discriminator Network: This network acts as a quality control. It analyzes the generated content from the generator network and determines how well it mimics real data. Through an iterative process, the generator and discriminator networks “compete,” with the generator constantly refining its creations to fool the discriminator. This ongoing competition leads to increasingly sophisticated and realistic deepfakes.

Deepfakes Vs. Other Fraudulent Activities

While deepfakes pose a significant threat, it’s important to distinguish them from other fraudulent activities:

  • Presentation Attacks: These involve using readily available photos or videos of the target individual during identity verification (e.g., holding a printed photo in front of a camera). Deepfakes, on the other hand, create entirely synthetic content that can be far more convincing.
  • Spoof Attacks: Similar to presentation attacks, spoof attacks utilize real images or videos of the target person to deceive facial recognition systems. However, spoof attacks typically focus on exploiting vulnerabilities in the recognition technology itself, while deepfakes can bypass such systems altogether by creating entirely new, realistic representations.
  • Synthetic Identity Fraud: This broader category encompasses the creation of entirely fictitious identities for fraudulent purposes. Deepfakes can be a tool within this scheme, generating fake photos and videos to bolster the fabricated identity. However, synthetic identity fraud can also involve other tactics like creating fake documents or social media profiles.

Learn How to Prevent Deepfakes in The Age of Generative AI

The Threat Landscape: Deepfakes and Fraud

Beyond the hyper-realistic manipulation of visuals and audio, deepfakes pose a multifaceted threat landscape with far-reaching consequences. Let’s explore some real-world examples:

statistics related to impact of synthetic identity fraud

High-Profile Social Commentary 

Actor Jordan Peele’s use of deepfakes featuring Barack Obama served as a stark reminder of the technology’s ability to blur the lines between reality and fabrication. This incident highlights the potential for deepfakes to be used to spread misinformation and sow discord, particularly in sensitive political or social situations.

Political Manipulation 

Deepfakes have emerged as a weapon in the political arena. Imagine a deepfake video of a candidate making outrageous statements surfacing just before an election. This could erode public trust and sway voters. The 2019 deepfake featuring Mark Zuckerberg, falsely confessing to data manipulation, demonstrates this risk of deepfake attacks.


Deepfakes pose a significant threat to personal security. Malicious actors can create compromising deepfakes (often morphing faces into explicit content) to blackmail victims, particularly women, for emotional distress and financial gain.

Financial Extortion 

Deepfakes can be used to impersonate executives or other authorized personnel. Fraudsters can leverage these deepfakes to trick individuals or gain access to financial resources. 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.

These examples highlight the multifaceted threat landscape posed by deepfakes. They can be used to damage reputations, manipulate public opinion, and extract money from unsuspecting victims.

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 liveness involves 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

The fight against deepfakes and synthetic identity fraud extends beyond basic liveness checks to include advanced machine learning techniques and presentation attack detection mechanisms. Video Injection Attack Detection is a sophisticated technology that plays a crucial role in safeguarding organizations and individuals.

How KYC Vendors Can Prevent Deepfake Fraud in ID Verification?

The rise of deepfakes presents a significant challenge for Know Your Customer (KYC) vendors offering remote identity verification solutions. These hyper-realistic synthetic media can be used to impersonate legitimate users, potentially leading to identity fraud and financial losses, underscoring the need for sophisticated detection systems to prevent deepfakes.

Fortunately, KYC vendors can implement robust strategies (behavioural biometrics) to mitigate this risk.

Prioritize Live Video Capture 

Deepfakes are often crafted from readily available photos or videos of the target. Verification systems that allow users to upload their videos for verification are particularly vulnerable to deepfake threats. Moving away from such biometric systems is crucial to prevent deepfakes and ensure the security of digital identities. Instead, opt for solutions that utilize live video capture. This significantly reduces the risk of fraudsters submitting pre-recorded deepfakes to bypass verification.

Embrace Liveness Detection with Challenges 

Some sophisticated deepfakes can bypass basic liveness checks so try to implement advanced liveness detection technology that goes beyond basic checks, incorporating facial liveness detection to enhance security.

These advanced systems ask users to perform specific actions during video verification, such as blinking, turning their heads, or smiling. Analyzing these involuntary movements helps confirm the user’s physical presence prevent attempts to use static images or masks and even prevent video replay attempts.

Enforce Multi-Factor Authentication 

Don’t rely solely on video verification. Integrate multi-factor authentication (MFA) into your KYC process to reinforce defence against synthetic identity fraud and utilize biometric technology where possible. This adds an extra layer of security by requiring users to provide additional verification factors beyond just a video, such as one-time passcodes sent to their phones or answers to knowledge-based authentication questions. This multi-layered approach makes it significantly harder for deepfakes to bypass verification.

Partner with Reputable Verification Provider

Choose a verification provider with a proven track record of security and deepfake detection capabilities. Look for providers like Facia that utilize advanced technologies like Artificial Intelligence (AI) and video injection attack detection.

How Video Injection Attack Detection Stops Deepfakes?

While human eyes might miss them, VIAD (Video Injection Attack Detection) leverages the advanced machine learning model Morpheous to meticulously analyze video streams for subtle inconsistencies. These inconsistencies can expose pre-recorded videos, deepfakes injected into live feeds, or even manipulation attempts.

How VIAD Detecs Deepakes?

VIAD goes beyond basic recognition. Here’s how it exposes deepfakes:

  • Unnatural Lighting or Background Inconsistencies: Deepfakes often struggle to perfectly match lighting or background details. VIAD can identify these discrepancies, like a person with a beach background in a snowy video call.
  • Unnatural Facial Movements: Blinking too regularly or movements appearing too mechanical can be red flags for deepfakes. VIAD analyzes these patterns to detect inconsistencies.

VIAD offers additional layers of security:

  • Manipulated Video Streams: Imagine a fraudster replacing a board member with a deepfake during a video call. VIAD can identify these subtle anomalies and prevent unauthorized access.
  • Man-in-the-Middle Attacks: These attacks involve intercepting communication and injecting malicious content. VIAD can identify these disruptions, protecting your sensitive data.

Facia: Your Trusted Partner in Secure Authentication

Facia, a leading provider of liveness detection technology, offers the fastest and most secure solutions on the market. Their cutting-edge technology ensures seamless authentication while maintaining the highest level of accuracy.

Final Thoughts

Biometric liveness detection is constantly innovating. New advancements like passive liveness (analyzing involuntary movements) and AI-powered deepfake detection ensure this technology stays ahead of evolving threats.

By embracing liveness detection systems, businesses can create a more secure and trustworthy digital space, achieve enhanced security, improve customer experience through frictionless authentication, minimise financial losses due to fraud, and comply with user verification regulations

With Facia, businesses can experience a more secure and trustworthy digital space.

Frequently Asked Questions

How can I tell if a video is a deepfake?

Identifying a deepfake can be challenging as it becomes more sophisticated. However, there are several red flags to look out for:

  • Unnatural Blinking: Watch for irregular blinking patterns that don't seem natural.
  • Lighting and Skin Texture: Look for inconsistencies in lighting or skin texture across different parts of the video.
  • Lip Sync: Check if the lip movements align accurately with the spoken words.

These signs can help you determine if a video has been manipulated.

Which technique is used for deepfake detection?

To effectively detect deepfakes, advanced deep learning techniques and facial liveness detection systems are used. These methods train algorithms on large datasets of real and manipulated images and videos, employing machine learning to improve the detection of deepfake attacks. By analyzing this data, the algorithms learn to identify subtle patterns and inconsistencies that are invisible to humans. Convolutional Neural Networks (CNNs) and Autoencoders are some of the key deep-learning architectures used in this process.

Can AI detect deepfakes?

AI detects deepfakes by using generative AI and deep learning algorithms that analyze the characteristics of videos and images. These algorithms focus on inconsistencies in facial features, unnatural blinking patterns, and irregularities in voice recognition. AI-powered tools like the Facia deepfake detection software can perform identity verification to ensure the authenticity of the content.

How does liveness detection work?

Liveness detection counters presentation attacks (using photos, videos, or masks). Active liveness detection requires user actions like specific movements. Passive liveness detection uses algorithms to detect life signs (eye movements, expressions) without interaction. This enhances biometric security as it ensures the person in front of the camera is real and present during verification.

What's the difference between video injection attacks and presentation attacks?

Both video injection attacks and presentation attacks aim to bypass security measures that rely on video verification. However, they differ in their methods:

  • Video Injection Attack: This attack involves manipulating a live video stream. Malicious actors might inject pre-recorded footage or a deepfake video of an authorized person into the live feed to gain unauthorized access to a system or impersonate someone.
  • Presentation Attack: This attack involves presenting a fake image or video recording of a legitimate user to a verification system. Common examples include using a photo, a mask, or a pre-recorded video of the authorized person during verification.
Can video injection attacks be prevented?

Video injection attacks, where fake videos are inserted into a live feed, pose a serious threat. However, they can be effectively countered using a layered security strategy.

The first step is a vulnerability assessment. This helps identify weaknesses during video capture, such as susceptibility to pre-recorded footage or deepfakes.

Liveness detection is a critical defence mechanism. Our AI-powered systems analyze video signals for inconsistencies, such as deviations in data paths or sudden lighting changes. This helps prevent unauthorized access and ensures that only real users are granted access.