Active Liveness Detection

What is Liveness Detection?

The method of identifying live input from phoney or faked information in computer vision is known as liveness detection. Many systems that employ biometrics for identification, such as face recognition, rely on this as a critical security safeguard.

There are several methods for detecting liveness, but the most frequent is to employ infrared cameras. When an infrared photograph of a person’s face is obtained, living tissue reflects more light than false or faked tissue. This distinction can be utilised to identify whether or not the input is live.

Liveness detection is a crucial security mechanism for preventing spoofing attacks. You may confirm that the input is coming from a real person by utilising a liveness detector.

What is the definition of Active Liveness Detection?

Active liveness detection necessitates a user explicitly confirming his or her existence by engaging with the system as part of the process (as opposed to “I am not a robot”). Active liveness detection, in example, requires only two photos for processing. The first image is normally acquired instantly, while the second image is captured automatically in response to a natural head motion. A natural head movement, such “nod if agree,” is an intuitive user interaction.
Using motion-triggered picture capture prevents an attacker from presenting or exchanging various photographs, resulting in a non-smooth (i.e. “unnatural”) head movement.

Because a 3D face moves differently than a 2D snapshot, even when bent, the active liveness identification technology, which is based on motion flow and artificial intelligence, now analyses the movement between the two collected photographs. It then determines if the user attempting to authenticate is “live” or “fake”.
Other approaches require the user to blink, grin, or use their eyes to track dots on the screen. These strategies may be vulnerable to simple assaults or have a lack of usability.

What is Active Liveness detection?

When using facial biometrics for authentication, accuracy is no longer a concern. Spoofing attempts, on the other hand, represent a severe danger employing printed images, recordings, deep false pictures, and 3D masks. Facial Liveness Detection has specific characteristics to detect biometric spoofing attacks, which might be an impersonation of a person’s unique biometrics scanned by the biometric detector to fool or circumvent the system’s identification and authentication stages. Even though face recognition can consistently answer the question, “Is this the correct person?” but not, “Is this a real person?” liveness detection technology plays an important role in fraud identification and mitigation. To be trusted, face biometric matching must be capable of detecting spoofs, as well as to ensure the accuracy of our biometric data. In other words, liveness detection permits passive and active detection, removing the need for us to keep our biometrics hidden, which is a good thing given how many images and videos we save and share online.

Active and passive liveness detection are the two types. Active liveness detection necessitates the user doing an action, such as smiling, blinking, or speaking, to demonstrate that they are alive. The active technique is sometimes troublesome since fraudster identity is easily fooled in what is known as a “presentation assault.” This enables fraudsters to easily fool the system by employing a variety of gadgets or “artefacts,” some of which are relatively simple to deploy, such as 3D paper cutout masks.

Users must respond to “challenges” including head movements, smiling, and blinking, which requires time and effort. This approach is more dependable and trustworthy than passive liveness detection, but we have witnessed a shift from active solutions to today’s modern, passive liveness detection, which is being pushed by enterprises increasingly emphasising user experience as a strategy to acquire and retain clients. Active techniques are more effective.

What are the benefits of Active Liveness Detection?

In terms of data security and privacy, active liveness detection provides various advantages. Because active liveness detection is not achievable without the user’s awareness, it is particularly suited to services with a strong emphasis on data protection and security, such as GDPR, KYC, and AML scenarios, which all demand user engagement. An active liveness detection ensures that the user acknowledges, by some opt-in action, that he or she is operating consciously and freely.

Active liveness detection includes an optional challenge-response approach for further security and assurance. The user is asked to follow a series of random instructions (e.g. turning the head in one specific direction). The authentication is successful only if these “challenges” are appropriately completed.

The challenge-response process can be repeated depending on the amount of security desired. The greater the number of difficulties demanded, the higher the security level. The combination of liveness detection and challenge-response is a highly strong technique to accomplish ‘user consent,’ as needed by GDPR. Furthermore, when it comes to defending against deepfakes, this technology, when paired with virtual camera detection, may significantly increase security.

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