Liveness Detection

Prevent injection attacks and detect deepfakes with our advanced liveness detection, ensuring that biometric data used for authentication is from a genuine person. Combined with in-built MFA technology, Keyless ensures large-scale attacks are virtually impossible.

Liveness Detection

Prevent injection attacks and detect deepfakes with our advanced liveness detection, ensuring that biometric data used for authentication is from a genuine person. Combined with in-built MFA technology, Keyless ensures large-scale attacks are virtually impossible.

What is Liveness?

Liveness detection in biometric systems verifies that a biometric sample is from a real person, preventing attacks with photos or deepfakes, known as presentation attacks. There are two types of liveness detection.

Active liveness uses a challenge-response protocol, asking users to perform actions like blinking or nodding.

Passive liveness analyzes natural image or video traits—such as texture and reflections—without requiring user actions.

While both methods were traditionally effective, advanced deepfakes can now mimic active liveness protocols, making passive detection the preferred method for accurately identifying deepfakes.

Webinar: The Deepfake Dilemma

Listen to our Co-Founder and CTO Paolo Gasti explain deepfakes—their origins, the risks they pose, and the technologies designed to stop them.

Watch Webinar

Read Recap Blog

Detecting Deepfakes with Keyless

Keyless uses passive liveness detection to verify the identity of the person authenticating. While Keyless’ complete list of passive indicators remains confidential, here are some common traits that passive liveness detection technologies can detect.

Colors

Scrutinizing skin color tones and variations, and detecting anomalies that are often present in synthetic or manipulated images.

Patterns

he system examines textural and background patterns such as the moiré patterns that appear when a screen is seen from a screen.

Reflections

By analyzing how light reflects off the eyes and skin, passive liveness technologies can identify the natural interplay of light and shadow that are indicative of authenticity.

Periocular Region

Observing the area around the eyes and detecting unique micro-movements and traits that are challenging for deepfakes to replicate accurately.

Vein Recognition

Leveraging vein pattern analysis, a unique detail that significantly enhances the reliability of identity verification.

Vibration

The detection of minute vibrations or micro-movements associated with holding a phone offers another layer of authentication accuracy.

Movement Detection

Differentiating between a live person and still objects such as photos or phones.