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Liveness Detection: Your Guide to Protecting Biometric Systems

Biometric Post
Mar 21, 2023
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Biometric Post
Liveness Detection: Your Guide to Protecting Biometric Systems

Do you have a biometric system in your organization? A facial recognition system for office access control, a fingerprint scanner for banking eKYC, or an iris scan for identity check-ins? Whatever the application may be, it’s important to make sure your biometric system is secure and trustworthy. That’s where liveness detection comes in.

Liveness detection technology verifies the authenticity of biometric data by ensuring that it came from a living human, rather than a photograph, fake finger, or other manipulated media. In doing so, it prevents spoofing attacks from malicious actors who intend to misuse biometric data for their own benefit.

In this blog post, we’ll explore the fundamentals of liveness detection, how it works to protect your biometric systems and the various spoofing attacks that it can protect you against. Ready to make sure your biometric systems are secure and trustworthy? Let’s dive in.

🧐 What is Liveness Detection?

Liveness detection is a technology used to detect and verify whether the captured biometric data (such as facial images, fingerprints, or iris scans) came from a live person or a spoofed source.

It works by analyzing the data to determine if there are any “tells” that indicate a human is present, such as blinking eyes, the subtle movements that occur when a person turns their head slightly, or the sweat that would be present on a real finger. It also looks for inconsistencies that would indicate that a photo or other manipulated media was used, such as low resolution, lack of shadows, incorrect skin tone, or a discriminative spatiotemporal mapping of faces.

By detecting these tells and inconsistencies, liveness detection can distinguish between a real person and an image, video of a person, or a fabricated finger. This helps protect your biometric systems from spoofing attacks, which we’ll explore more in the next section.

What Are Biometric Spoofing Attacks (Presentation Attacks)?

Biometric spoofing attacks (or biometric presentation attacks) are fraud attempts to bypass a biometric system by presenting manipulated biometric data, such as a fake fingerprint or photograph, fooling a biometric system into thinking that it is a live and authorized user.

Biometric presentation attacks are classified into two types:

  • Impersonation attacks: These are the most straightforward kinds of presentation attacks. They present a fabricated biometric template or image that mimics the characteristics of a genuine, live person, such as a photo, voice sample, or artificial finger, to impersonate a legitimate user and bypass the security system.
  • Obfuscation attacks: These attacks attempt to obscure biometric data in order to disrupt the capture process. Obfuscation techniques include voice distortion, disguises (such as hats, wigs, and makeup), and even plastic surgery to avoid a biometric system from detecting a user's true identity.

Biometric spoofing attacks may be used to gain access to services, steal sensitive information, gain access to restricted areas, or even take control of a system. As malicious actors realize the potential for financial gain or other benefits from breaking into biometric systems, these attacks are becoming more common.

5 Main Types of Biometric Spoofing Attacks

Some of the most widely known spoofing attacks seen today are as follows:

1) Fingerprint spoof attacks:

These attacks involve fabricating a fake fingerprint to gain unauthorized access. A fake finger could be made from a variety of materials, including plastic, latex, or even gelatin.

2) Face spoof attacks:

These attacks attempt to fool face recognition systems by presenting a manipulated face of a legitimate user. This could be done with a photograph, video replay, 3D-printed mask, 3D-rendered model, and more.

Face spoof attacks with a photograph and video replay.
Face spoof attacks with a photograph and video replay

3) Iris spoof attacks:

This type of presentation attack aims to bypass iris recognition systems by presenting a manipulated printed image of a legitimate user’s eye. This could be done with a photograph, video, or contact lenses.

4) Voice spoof attacks:

Voice spoof attempts to bypass voice authentication systems by replicating the voice of an authorized user.

5) Deepfakes:

Deepfakes are highly realistic videos or images produced by deep learning and artificial intelligence that mimic someone's face, voice, and other biometrics. Deepfakes can be difficult to detect due to their high level of realism.

A deepfake example: Face2Face
A deepfake example

These types of presentation attacks can be devastating, as they are hard to spot and may lead to identity theft, fraudulence, or other malicious activities. Fortunately, we have liveness detection technology that works efficiently to protect biometric systems from these threats.

How Does Liveness Detection Work? The Anti-Spoofing Technology

Liveness detection is developed to detect presentation attacks by recognizing the subtle characteristics of a live person. These presentation attack detection (PAD) techniques work by using various algorithms and sensors to identify a live face, fingerprint, voice, or other biometric data.

For example, in facial recognition systems, liveness detection algorithms can detect subtle facial movements, such as blinking and head rotation, to determine whether a face is live or spoofed. The same goes for fingerprint recognition systems, where liveness detection algorithms analyze the pressure, texture, and sweat levels of a fingerprint to determine whether it belongs to a live person.

Liveness detection techniques are designed to detect spoofing attempts in real-time, ensuring the highest level of security for biometric systems. Today, these anti-spoofing methods can be seen as follows:

1) Face liveness detection methods:

To detect facial presentation attacks, liveness detection methods use 2D/3D facial recognition technology, motion tracking algorithms, and even thermal imaging techniques to detect subtle variations in a person’s face.

There are two types of face recognition liveness detection:

  • Active liveness detection: An active liveness check requires users to perform specific actions (such as blinking, head tilting, or smiling) to prove their liveness.
  • Passive liveness detection: Passive liveness detection, on the other hand, doesn't need any action from the user. Instead, it analyzes a person’s face in real-time to detect liveness. Since it works in the background, it is a more user-friendly option than active liveness detection. Furthermore, the use of deep learning algorithms and artificial intelligence in passive liveness detection has made it far more efficient at detecting any spoofing attempts, bringing a higher level of security to video surveillance applications.

2) Fingerprint liveness detection methods:

Fingerprint liveness detection methods are designed to be able to detect spoofing attempts by analyzing certain features of a live fingerprint. Such features can include texture, sweat levels, odor, and blood pressure to determine whether a fingerprint belongs to a live person or not.

There are two types of fingerprint liveness detection techniques:

  • Hardware-based fingerprint liveness detection: The hardware-based techniques require additional sensor devices such as electronic noses and pulse oximeters to detect liveness.
  • Software-based fingerprint liveness detection: Software-based techniques, on the other hand, use image-processing algorithms to analyze a single fingerprint image and determine liveness. Software-based fingerprint liveness detection techniques can further be classified into two categories: static and dynamic.

  1. Static methods: Static liveness detection methods allow for fast and effective analysis of a single 2D fingerprint image by examining the various features such as textures, sweat pores, skin elasticity, and spatial surface coarseness. This method is a great option to ensure optimal accuracy with only minimal time needed for computation. With just one image required from a live person, static liveness detection technology can quickly identify whether it belongs to an actual individual or not.
  1. Dynamic methods: On the other hand, dynamic methods analyze a variety of features from multiple 2D fingerprint scans collected over time to determine liveness. This strategy can detect any static elements but also changes in those components across various scans. For example, it evaluates skin deformation, blood flow, and perspiration produced during the scan period to see whether or not the print is genuine. Compared to static methods, dynamic methods produce more accurate results; however, they take longer to process.

In general, hardware-based techniques are more expensive and limited to specific spoofing materials, whereas software-based techniques are more general and can handle spoofing material variations with greater versatility.

For real-world applications, software-based techniques are the go-to option since they make use of existing biometric devices and only need to incorporate image-processing algorithms. This makes them a cost-effective, efficient solution for organizations that require liveness detection added to their biometric systems.

Aratek fingerprint scanners with liveness detection technology
Aratek fingerprint scanners with liveness detection technology

3) Iris liveness detection methods:

Iris liveness detection is a technique for determining whether or not an eye belongs to a living person. By analyzing subtle changes such as pupil dilation, focus, and movement features, it can ensure that the eye used in identity verification and authentication is genuine rather than a photograph or artificial contact lens.

There are two types of iris-liveness detection techniques:

  • Hardware-based iris liveness detection: To detect an eye's liveness, these methods require additional sensors such as multispectral cameras, 3D depth sensors, or electrooculography (EOG) sensors. EOG sensors, for example, measure electrical activity in the eye and can determine whether or not it is responding to external stimuli, ensuring that the eye belongs to a living person.
  • Software-based iris liveness detection: Software-based iris liveness detection can detect whether someone is a living person or not through their single 2D iris image (static approach) or multiple images (dynamic approach). This technology analyzes imperfections, color distribution, texture, and other important attributes that are near impossible to replicate in photos or contact lenses. In addition, advanced machine learning and deep learning techniques help improve accuracy further.

4) Voice liveness detection methods:

Voice liveness detection is an important security measure that can prevent malicious actors from using voice spoofing attacks to gain access to automatic speaker verification (ASV) systems. This technology works by detecting the unique characteristics of a live voice and distinguishing it from synthetic, replayed, or converted audio. Through advanced algorithms and machine learning techniques, it can analyze various features such as speaking rate, pitch, cadence, pauses, and other features to determine whether or not the speaker is real. It also monitors for any signs of tampering or manipulation, such as background noise, sound effects, and other distortions that could indicate a spoofing attack.

There are several voice liveness detection methods available, including Void (voice liveness detection), VoiceGesture, LiveEar, VibLive, and others, that can be used to combat various voice presentation attacks in different systems, including smartphones, voice assistants, IoT devices, and more.

For any organization attempting to preserve the security and integrity of its biometric systems, liveness detection is a must-have technology. This advanced protective measure will detect attempts at spoofing with face masks, fake fingers, synthetic voices, and more - providing an additional layer of defense for your organization. We'll investigate why this form of protection is essential today as well as all the advantages it can bring.

Why Does Your Organization Need Liveness Detection?

Liveness detection is critical in ensuring the trustworthiness and security of biometric systems, which serve as the foundation for a wide range of digital identity applications, ranging from cybersecurity, and physical access control to online banking. With sophisticated spoofing techniques on the horizon, liveness detection serves as the last line of defense against any potential fraud.

In recent years, more and more cases of identity theft and biometric fraud have come to light, for example:

In March 2019, a criminal used artificial intelligence-based software to imitate the voice of a chief executive and demanded €220,000 ($243,000).
In 2021, cybercriminals used silicon thumbs to clone thumb impressions from sale deeds available on various websites and steal money from Aadhaar-linked bank accounts. According to reports, 468 such complaints have been reported in Haryana with 18 cyber criminals arrested and Rs 14.64 lakh recovered as a result.

These spoofing attacks resulted in a heightened awareness of how important liveness detection is for the safety and security of biometric systems.

Amid the COVID-19 crisis, where almost all services and transactions have shifted to online platforms, identity theft or biometric fraud has become a major threat. This makes it even more essential to be able to accurately identify people from remote locations. Because of this, incorporating liveness detection into the digital onboarding and authentication process is essential to ensure the safety and trustworthiness of your biometric systems, especially in the digital age.

Utilizing liveness detection offers numerous advantages to organizations, such as reducing fraud, boosting customer trust and satisfaction, improving accuracy levels, enhancing security measures, and much more. Below, we will explore the various advantages of using liveness detection.

Advantages of Using Liveness Detection:

1. Reduce fraud and Increased security:

By using liveness detection, organizations can prevent hackers and criminals from using stolen photos, masks, or other objects to gain access to confidential information or accounts, providing an extra layer of security and helping to reduce fraud.

2. Improved accuracy:

Liveness detection can confirm that only genuine users have access to the system, minimizing false positives. Furthermore, this guarantees a secure enrollment process by capturing and securely storing reliable biometric data, creating trustworthiness and assurance in your system.

3. Boost customer trust and satisfaction:

By using liveness detection, businesses can demonstrate to their customers that their services are secure and reliable. This helps build customer trust and satisfaction in the organization's security measures, which is important for any business that wants to build long-term relationships with its customers.

4. Cost savings:

By adding liveness detection to the authentication process, organizations like governments, hospitals, and banks can greatly reduce the amount of money they lose to fraud and other security breaches. Also, they will save money that would have been spent on manual identity checks and other extra security measures. This has the potential to boost profits and keep them ahead of the competition.

Biometric Spoofing vs. Liveness Detection: A Never-Ending War

As digital identity and biometric technologies continue to advance and reshape the world, we are witnessing a global shift in how services will be conducted. From government welfare programs to healthcare, banking, and beyond, our lives have become increasingly intertwined with digital technologies. Unfortunately, the more our biometric information is used for various purposes, the more valuable it becomes. This makes our biometric data vulnerable to hackers and other criminals who want to use it for their own gain.

It's become essential to make sure our digital identities are protected and secure, which is why liveness detection is so important.

As technology leaps forward, especially in the realm of AI and deep learning, liveness detection methods are becoming ever more sophisticated. However, these same advancements can be used to spoof biometric data with increasing proficiency - making it even harder for people to recognize presentation attacks.

It's like a never-ending game of cat and mouse.

The bottom line? This constant battle between biometric spoofing and liveness detection means that organizations must stay informed and up-to-date on the latest developments in both areas. By being proactive and investing in the right liveness detection solutions, organizations can keep their biometric systems secure and reliable.


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