Blog 20 Sep 2023
Presentation Attack Detection Solution For Secure Identity Verification

Presentation Attack Detection Solution For Secure Identity Verification

Author: Luke Oliver | 20 Sep 2023

Identity verification has become a necessity in the contemporary digital world. Biometric systems are at the heart of authentication measures, such as unlocking phones, secure transactions and customer onboarding.

Organisations have realised that in order to enhance security, their verification methods are required to be equipped with the most sophisticated technology to prevent cases of identity theft. As fraudulent activities become more sophisticated, traditional identity verification methods no longer suffice.

Presentation Attack Detection (PAD), complemented by liveness detection, emerges as a critical component in this security framework, ensuring that digital interactions remain both seamless and secure against the backdrop of escalating identity theft attempts.

What is a Presentation Attack?

Presentation attacks, often referred to as “spoofing attacks,” involve presenting fake biometric traits to deceive biometric systems. For example: a hyper-realistic mask designed to mimic human facial features or even a high-definition recording of an individual’s voice to trick voice recognition software.

These are not scenes from a sci-fi movie; they are real challenges that ID verification systems face daily. According to a study by the National Institute of Standards and Technology (NIST) in 2020, advanced facial recognition systems, once considered foolproof, exhibited error rates between 5% to 50% when faced with presentation attacks.

It is an alarming reminder of how even the most advanced systems can be vulnerable to sophisticated attacks. To get a deeper insight into how innovative solutions are stepping up against these challenges, check out our video Facia Unveils How It Will Defend Against $24B Identity Thefts: From Paper Masks to Deepfakes.

Implications for Businesses and the Importance of ID Verification

Businesses operating in the B2B industry have a greater responsibility towards Presentation Attack Detection. Imagine the damage if unauthorized personnel gained access to sensitive corporate data or if fraudulent transactions were made simply because a biometric system couldn’t distinguish between a genuine user and a fake.

Beyond the immediate financial implications, such breaches decrease trust, which is a key component for any business. In a survey conducted by IBM in 2021, the average cost of a data breach was estimated to be $4.24 million, hitting a 17-year high.

Such breaches have implications that extend further than just financial loss, they also induce reputational damage, loss of customer trust, and potential legal ramifications. With the growing interconnectedness of businesses globally, ID verification is more than a security measure, it’s a testament to a company’s commitment to safeguarding its stakeholders. 

In essence, robust biometric systems serve as the first line of defense against a gamut of threats lurking in the digital shadows. PAD methods involving 3D Liveness Detection aim to distinguish between genuine biometric traits and presentation attack artefacts. It’s the frontline defense against spoofing attacks, ensuring that only genuine users gain access.

PAD vs Liveness Detection

While both PAD and liveness detection serve to counteract spoofing attacks, they differ in their approach. Liveness detection primarily ensures that the biometric being presented is “alive” or genuine. In contrast, PAD focuses on identifying whether the biometric trait is authentic or a fraudulent representation. 

Facia offers an AI-driven liveness detection solution that is capable of preventing presentation attacks. Facia meets the highest standards by meeting the requirements for iBeta’s ISO 30107-3 Presentation Attack Detection Compliance.

Strategies to Counteract Presentation Attacks

In the realm of biometric identity verification, defending against presentation and spoofing attacks requires a blend of cutting-edge technology and informed strategies. 

Let’s delve into the current vulnerabilities of biometric systems and then determine how businesses can improve their security.

Biometric Presentation Vulnerabilities

Every technological advancement brings about its set of vulnerabilities. Biometric systems, though lauded for their precision and user-friendliness, are no exception. Their vulnerabilities range from easily replicated fingerprints to manipulated facial recognition data using high-definition photos.

As discussed in the NIST report error rates range from 5% to 50% when confronted with presentation attacks. Such vulnerabilities underscore the pressing need for robust countermeasures.

Innovations in Countering Presentation Attacks

Here are some key innovative measures that can help prevent presentation attacks:

Liveness Detection: 

Liveness Detection can distinguish between a live human and a replica, effectively discerning a real face from a high-definition photo or mask.

Multi-Factor Authentication: 

Introducing multiple layers of verification, such as combining facial recognition with a PIN, substantially reduces the chances of a successful breach.

Behavioral Biometrics: 

By analyzing patterns like keystrokes or the manner in which a device is held, behavioural biometrics provides an additional layer of security.

Best Practices to Ensure Secure Biometric Verification

Here are some practices that businesses can adopt to ensure secure biometric verification

Adopt Updated Systems:

Ensure that biometric software undergoes regular updates to address emerging vulnerabilities.

Promote Awareness:

Continuously educate employees about the latest threats and the causes of presentation attacks.

Engage Expertise: 

Consider collaborating with biometric security consultants or firms to conduct periodic reviews of the system’s robustness.

Data Encryption: 

Ensure that biometric data storage adheres to the highest encryption standards, making it challenging for fraudsters to access or manipulate sensitive information.

Dissecting PAD Techniques in Facial Recognition

In facial recognition, the spotlight isn’t merely on identifying faces, but on discerning genuine ones from deceptive replicas. Presentation Attack Detection Tool provides vital protection against deceptive attacks. There are two major categories of Liveness; Active and Passive

Deep Dive into PAD: Active vs. Passive

Active PAD: 

Active Presentation Attack Detection is when a user is asked to perform specific actions such as blinking or nodding. 

How Does It Work?

It tracks human-like movements and compares it with the given prompt. It also uses structured light projections or flash illuminations to distinguish a genuine face from a spoofing attempt. 

Real-World Applications: 

Active PAD is widely implemented in high-security zones, financial transaction systems, and advanced mobile device authentications.

Passive PAD:

Instead of initiating interaction, passive techniques analyse facial features with a face mapping technology to determine whether it is a genuine individual or not. 


Passive PAD leans heavily on analyzing inherent facial properties. It might check for natural skin texture, blood flow patterns, or the subtle sheen of genuine skin compared to a photograph or mask.


Passive PAD offers a seamless user experience, as individuals aren’t prompted for any conscious actions. This makes it ideal for user-friendly applications.


Ideal for public surveillance, crowd control, or places where uninterrupted flow is crucial. Think airport security checks or large-scale event entries where active interactions could cause bottlenecks.

The Convergence

For enhanced security, organisations do not confine themselves to one technique. A hybrid approach, combining the strengths of both active and passive PAD is becoming the norm in areas demanding high security coupled with user convenience.

Different Levels of Presentation Attacks in Facial Recognition

As facial recognition technologies increase in sophistication, so do the techniques wielded by malicious actors. It’s crucial for businesses and consumers alike to understand the spectrum of presentation attacks, which vary in complexity and ingenuity.

Level 1: Surface-level Threats 


These are the basic attempts to deceive facial recognition systems, often involving printed photographs or videos played on a digital device.


Although many modern systems can now detect such attempts, underestimating these attacks can still pose risks, especially for outdated or low-tier systems.

Level 2: Intermediate Threats 


A step above the basics, these attacks involve more detailed replicas such as high-quality 2D masks or animated digital avatars.


These threats can bypass certain systems, especially those lacking advanced PAD capabilities. They underscore the need for continuous tech updates in security protocols.

Level 3: Advanced Threats & Deepfakes


With the rise of AI, deepfake technology has become a  very common form of presentation attacks. It involves creating hyper-realistic, AI-generated video footage or 3D face models, sometimes indistinguishable from real humans.


Deepfakes present an alarming challenge for facial recognition. Their convincing nature can facilitate unauthorized access, identity theft, or even spread misinformation on social platforms.

Addressing the Threat Landscape

As businesses invest in facial recognition, understanding these threat levels is paramount. For instance, a financial institution’s facial authentication for high-value transactions might prioritize defenses against Level 3 attacks, while a casual dating app may be more focused on the widespread Level 1 threat.

However, with the democratisation of technology and the rapid advancements in AI, even the most basic systems must be prepared for future threats. Researchers and tech firms are in a continuous race against time, ensuring that every evolution in presentation attack is met with a more potent countermeasure.

Real-World Applications of PAD:

In the expanding arena of biometric security, the effectiveness of Presentation Attack Detection (PAD) systems is not just a matter of technological prowess—it is the backbone of real-world applications, particularly in the B2B sector. 

Here, we explore how PAD serves varied sectors, creating a fortress of security and trust.

Access Control: Balancing Convenience with Security

Companies around the globe are rapidly transitioning from traditional access methods like keycards or passwords to biometric-based access. With PAD in place, businesses can confidently minimise risks like unauthorised access using photographs or 3D-printed models. 

The technology ensures that only genuine, live users gain entry, making facilities more secure and operations more efficient.

Payments/Point of Sale: From Face ID to Fingerprints

As digital payments become the norm, it is important to ensure that these transactions are genuine. With companies like Apple already employing Face ID for payment authentications, the onus of detecting presentation attacks has never been higher. 

PAD techniques ensure that a high-resolution image or 3D replica cannot authorise a transaction, thus safeguarding both businesses and consumers from potential fraud.

Air Travel: The Next Generation of Security Protocols

Airports worldwide are embracing facial recognition as a means to expedite passenger processing without compromising on security. However, with concerns about potential misuse or deception, PAD comes to the forefront. 

It ensures that passengers moving through automated gates are genuinely who they claim to be, preventing any potential security breaches that could have catastrophic consequences on such a massive scale

Identity Validation (KYC): The Foundation for Trust in a Digital Business World

Know Your Customer (KYC) processes are fundamental in sectors like banking, where verifying the identity of customers is not just a matter of security but a regulatory requirement. As businesses employ facial recognition for remote KYC processes, PAD ensures these verifications are resistant to any presentation attacks.

Major ID Verification Providers Against Presentation Attacks in Facial Recognition

In the evolving landscape of biometric security, facial recognition stands at the forefront, holding immense promise and, simultaneously, challenges. With presentation attacks becoming increasingly advanced, the spotlight shifts to major ID verification providers. 

Which ones truly defend against these advanced threats? Let’s look at a comparative analysis to uncover the industry benchmarks.

Facia’s Foray into Facial Recognition

Facia’s solution is anchored around a dynamic liveness detection system, adept at differentiating between a genuine human face and high-fidelity deceptions like 3D masks or photographs. This potent layer of defence ensures that only real, live users get verified.

Best PAD Tool: The Ultimate Software for Biometric Security

Why Choose FACIA's Liveness Detection Software
When it comes to top-tier biometric security, Facia’s Liveness Detection stands out in the PAD landscape. Here’s a snapshot of its unparalleled offerings:

  • Incorporates Passive Verification, Face Mapping and Authentication, and 3D micro-movement detection
  • Facia Offers both active and passive preference modes, catering to diverse business needs
  • Facia’s solution response time of just 1 second with unparalleled accuracy
  • Boasts industry-leading accuracy rates, effectively countering spoofing attacks of more than 60 types
  • Facia’s tool is iBeta and NIST-certified

Final Word: The Dual-Edged Sword of Modern Biometrics

The emergence of biometric systems in today’s B2B landscape brings transformative benefits. However, with every innovation, there are potential challenges.  Facia stands at the forefront of this defence, turning anticipation into action. With its state-of-the-art PAD, it not only recognizes the genuine nature of every face and transaction but continually evolves to keep businesses a step ahead of emerging threats. To learn more about integrating PAD into your business, get in touch with us

Frequently Asked Questions

What is PAD in biometrics?

Presentation Attack Detection in biometrics is a crucial aspect that focuses on discerning genuine biometric presentations from deceptive attempts. It strives to uphold the integrity of biometric systems by identifying interferences or abnormalities during the biometric data capture process.

What is face presentation attack detection (PAD)?

Face Presentation Attack Detection (PAD) is an advanced security feature, often termed anti-spoofing, designed to safeguard face recognition systems. It works diligently to identify and mitigate attempts that seek to deceive the system using various means such as photos, videos, masks, or other synthetic reproductions of facial features.

What is the meaning of presentation attack?

A presentation attack refers to the act of maliciously presenting fabricated or manipulated biometric traits with the intent to deceive or disrupt the regular operation of a biometric system. It is a calculated attempt to compromise the system’s reliability and accuracy.

Which attacks are forms of presentation attacks?

Presentation attacks often involve deceptive materials like counterfeit fingerprints or fabricated facial images, intending to mislead biometric systems. As technology evolves, the nature of presentation attacks becomes increasingly sophisticated, encompassing a broader array of strategies to manipulate biometric verifications.

What is the difference between active liveness and passive liveness?

Active liveness detection involves explicit user actions, such as blinking, to confirm the presence of live biometric traits. In contrast, passive liveness detection operates subtly by analyzing spontaneous facial attributes and movements, ensuring a seamless and user-friendly authentication experience.

How do presentation attack detection and liveness detection differentiate?

Presentation attack detection (PAD) zeroes in on recognizing and countering deceptive materials or methods used in authentication processes. Liveness detection, on the other hand, concentrates specifically on ascertaining the presence of live, genuine biometric traits during the authentication procedure, ensuring the vitality of the presented characteristic.