Unlocking accounts with biometrics — such as facial or voice verification — is a fast, convenient, and frictionless way to replace the use of passwords, PINs, and security questions, making it easier for users to access their accounts. Biometric authentication has seen widespread adoption across banking, fintech, telecommunications, enterprise platforms and more.
As this adoption has grown, so have the threats. These systems are highly convenient but also create new attack surfaces. Fraudsters are increasingly targeting biometric systems with attacks that range from simple to highly sophisticated, including physical attacks like 3D masks or replayed videos, to AI-generated deepfakes and synthetic voices.
What is biometric liveness detection?
Biometric liveness detection is a key defense against these threats. By confirming that the biometric input is coming from a live person, rather than being spoofed or manipulated, liveness detection enables providers to detect and thwart these attacks, even as they evolve.
In this blog, we will explore how biometric liveness detection works, the types of fraud it is able to prevent, and how it fits into a broad, layered fraud defense strategy.
The role of biometric liveness detection in safeguarding authentication
Without biometric liveness detection, authentication systems could be tricked by even the simplest spoofs, like the use of a photo or a video recording. This makes biometric liveness detection a fundamental building block for securing biometric authentication. It gives organizations the ability to confirm that the person behind the login is real, present, and actively engaged.
Biometric liveness detection protects businesses from financial loss, as well as reputational damage. It also protects their customers from account takeovers and identity theft that can result in financial loss and long-term problems. Liveness detection is most effective when combined with additional layers of identify security across an organization, including document verification, behavioral biometrics, and contextual risk analysis and scoring. In this layered approach, the institution and its customers are protected against fraud attempts whether they are basic or highly advanced.
How biometric liveness detection works
With biometric liveness detection, a biometric sample provided by the user (like their face, or their voice) is verified as coming from a live human being at the time of capture. It helps systems distinguish between real-time, genuine input, and fraudulent artifacts being used to impersonate a user.
Biometric liveness detection applies to multiple modalities. For example:
- Facial liveness detection is used to confirm that a face is genuine and that a fraudster is not presenting a static photo, pre-recorded video, or a 3D mask.
- Voice liveness detection checks that a voice sample is live and spontaneous speech, rather than a recording or an AI-generated sample.
When embedded into biometric authentication, both facial liveness detection and voice liveness detection enable organizations to create a more effective safeguard against the most commonly executed forms of biometric fraud. Liveness detection techniques fall into two broad categories:
- Active liveness detection requires the user to participate in the process. The system may ask the user to blink, smile, repeat a specific phrase, or turn their head. With a challenge-response task, the system can verify that the subject is live and responding in real-time.
- Passive liveness detection works in the background and requires no user action - or even their knowledge that it is operating. It analyzes micro-expressions, depth cues, textures, light reflections, and other subtle signals. Passive methods have become increasingly popular for their ability to deliver stronger security while maintaining a frictionless user experience.
Underlying both active and passive liveness detection are sophisticated, AI-based detection algorithms. These algorithms are capable of spotting artifacts that indicate inconsistencies and are characteristic of synthetic images or voices, evaluating movement to determine that it is natural and human, and performing texture analyses to pick up subtle differences that might be found between real skin and a print or digital image.
The systems work in real-time, confirming liveness detection instantly - which is necessary for both security and convenience.
Why biometric liveness detection is essential
Liveness detection strengthens biometric systems by ensuring that authentication is only permitted in the presence of a live person. Without this step, fraudsters could easily bypass the system.
There are many examples of biometric attacks that could succeed in the absence of liveness detection. Some are simple, like a fraudster holding a printed photo or a phone replaying a video of the target. Others are more complex, like the use of a silicone mask to replicate another person’s facial features. A new generation of AI-powered attacks has come to the forefront, for example, with AI-generated speech expanding the ability of attackers to create nearly natural voice samples to bypass voice verification.
Liveness detection is able to block these attacks before they ever reach an organization’s systems. As a first line of defense, it ensures that only live and genuine biometric samples proceed for processing.
How biometric liveness detection can help prevent fraud threats
Presentation attacks
The most common form of biometric fraud is a presentation attack - where attackers physically present a form of fake biometric data to the system’s sensor. Examples of presentation attacks include:
- Holding a printed photo up to a webcam.
- Replaying a video which shows the target speaking or moving.
- Using a realistic, 3D-printed mask or other prosthetic device that mimics the target’s facial features.
Liveness detection is a highly effective tool for blocking these low- to mid-sophistication attacks that constitute the bulk of attempted fraud.
Other attacks
New techniques, some powered by emerging AI technologies, have empowered attackers to develop even more advanced strategies that go beyond presentation attacks. These include:
- Injection attacks, where manipulated or synthetic content is fed directly into a system’s data stream, bypassing the system camera or microphone.
- Deepfake video and voice cloning, where AI-generated synthetic media is used to create a convincing copy of a person’s face or voice.
- Hybrid attacks that combine multiple techniques discussed here, like the use of injected deepfake video with manipulated audio.
Biometric liveness detection is part of a layered strategy used to combat these sophisticated attacks. When liveness detection is combined with fraud analytics, device intelligence, and machine learning algorithms that adapt to emergent threats, businesses can deploy a comprehensive defense.
Real-world use cases for biometric liveness detection
Biometric liveness detection is beneficial for many services where the ability to securely verify identity remotely is needed. These include:
- Remote onboarding: Financial institutions and fintechs, as well as telecoms, routinely rely on liveness detection to verify that their new customers are who they say they are, rather than stolen or synthetic identities being used to open fraudulent accounts.
- Account recovery and step-up authentication: Liveness adds an additional layer of security when users request a password reset or perform another sensitive action like an address or phone number change.
- Payment and transaction approval: High-risk, or high-value, transactions are commonly gated with biometric authentication enhanced with liveness detection.
- Workforce authentication: Enterprises as well as government agencies frequently use liveness detection to ensure the security of employee logins and other types of access control, particularly in hybrid or remote environments.
Benefits and limitations of biometric liveness detection
Key benefits
Security
Biometric liveness detection delivers stronger protection against spoofing attempts, such as the use of printed photos, replayed videos, or prosthetics and masks. By confirming the physical presence of the user, biometric liveness detection is a fundamental layer of protection in modern fraud prevention strategies.
Compliance
Liveness detection can provide compliance with international standards for presentation attack detection, like ISO/IEC 30107. It ensures organizations are meeting regulatory requirements for identity verification processes.
Fraud reduction
Liveness detection reduces occurrences of account takeovers and account opening fraud by filtering out spoofed or synthetic attempts early on in the process. This provides strong protection for consumers’ accounts and identities, while also lowers the operational cost of fraud management and overall losses from fraud.
User convenience
Passive liveness detection techniques are seamless; they work in the background and require no additional effort on the user’s part. They enhance security while still delivering an authentication experience that is smooth and frictionless.
Limitations
Liveness detection provides strong protection against basic spoofing attacks, but as mentioned earlier, protection against highly advanced attacks benefits from layered defenses to detect and stop threats.
Performance of liveness detection software can also be impacted by low-quality cameras, poor lighting, and network conditions, any one of which may introduce unexpected shadows and artifacts. Additionally, care is needed to ensure that no demographic performance gaps exist so that all customers have an equally seamless experience.
Future outlook for biometric liveness detection
Biometric liveness detection continues to evolve as fraudsters adopt more advanced techniques. Liveness detection providers are integrating multimodal inputs, such as face, voice, and behavioral biometrics, alongside the use of advanced AI models that can spot deepfake artifacts.
As injection attacks and synthetic media become more common, the industry is also pushing toward stronger ability to detect and prevent them.
Conclusion: why biometric liveness detection matters
Biometric authentication is popular with customers and businesses alike. It offers convenience and security. But, without liveness detection, biometric authentication is vulnerable to exploitation. Liveness detection provides essential assurance that a real, live person is behind the authentication attempt, rather than a fraudster using a form of spoofing or synthetic media.
The most effective use of liveness detection is within a layered security framework, where it is reinforced by fraud analytics, document verification, advanced AI capabilities, and machine learning that adapts to emergent attacks.
Solutions like Mitek’s IDLive Face Plus and IDLive Voice make it possible for organizations to seamlessly combine world-class fraud detection tools with user-friendly and seamless authentication. If you’re ready to strengthen your identity verification process, contact Mitek to learn how liveness detection can help.
About Anastasia Molotkova - Product Manager at Mitek
Anastasia Molotkova is a certified product leader, specializing in AI-driven cybersecurity solutions that address emerging threats like deepfakes and generative AI fraud. She leads the development of innovative biometric technologies, including injection attack detection, deepfake detection and facial liveness detection, helping to set new security standards in digital onboarding and authentication.