Liveness Detection

Trust that only real, live people are verified with Incode’s advanced liveness detection technology.Trust that only real, live people are verified with Incode’s advanced liveness detection technology.

Leading companies trust Incode’s liveness detection technology

Business risks

Evolving fraud puts your business and customers at risk

Generative AI is driving more sophisticated identity fraud and deepfakes, making it harder than ever to differentiate between legitimate users and fraudsters. The financial and reputational damage this can cause puts your business at serious risk. But with threats advancing at such an alarming rate, most identity verification approaches are unable to keep up.

21X surge in deepfake fraud attempts over the last 3 years

70% of businesses claim
identity fraud losses have
increased in recent years*

Stop fraud at speed

Safeguard your business with instant liveness detection, starting today.

Facing fraudsters

The person behind the camera could be a fake

They could be manipulating your systems to appear as someone else, or they might not be a live person at all.

Common attacks

Deepfake and digital attacks

AI-generated deepfakes and advanced digital manipulation techniques. Digital attacks attempt to bypass identity verification software using digital manipulation, including AI-generated deepfakes.

Face swapping

Replacing the face in a target image with a face from another source.

Face morph

Combining two faces to create an entirely new, blended face.

2D Synthetic assets

AI-generated images of people who don’t exist – all facial features are entirely synthetically generated, unlike face morphs that blend real people’s facial features to generate a new face.

Face reenactment

Recreating characteristic movements and facial expressions to make a deepfake more convincing.

Video replays

Footage is replayed on high-resolution screens in front of the camera to simulate a live individual, making it difficult to distinguish between real and live people.

Common attacks

Physical presentation attacks

Counterfeit visual data that deceives camera-based verification systems. Physical presentation attacks involve manipulating visual data with high-quality screens or printed images to trick camera-based verification systems.

2D masks

Flat, printed masks are used to mimic someone’s face and deceive verification systems, but lack the depth and detail of a real human face.

3D masks

Lifelike, three-dimensional masks made of materials like silicone are crafted to closely resemble a real person, making them harder to detect.

Paper printouts

Printed photos of a face are presented to the camera, attempting to pass off a static image as a live individual.

Cardboards

Printed images mounted on cardboard provide a sturdier, more rigid appearance in an effort to fool systems.

Video replays

Footage is replayed on high-resolution screens in front of the camera to simulate a live individual, making it difficult to distinguish between real and live people.

Common attacks

Evasion attacks

Facial modifications that target and aim to deceive recognition systems. Evasion or obfuscation attacks occur when individuals alter their appearance to make it harder for facial recognition systems to verify their biometric features.

Exaggerated expressions

Overly dramatic facial movements, like extreme smiles or wide eyes, are used to distort the face and confuse recognition systems

Occluding objects

Items like hats, glasses, or scarves are used to block parts of the face, preventing systems from capturing a clear image.

Heavy makeup

Complex makeup techniques are applied to change the appearance of key facial features, making it harder for systems to recognize the individual.

User experience

Liveness detection with Incode Deepsight

Our advanced liveness detection within Incode Deepsight verifies real people in manipulated or synthetic footage, with no friction or user interaction needed.

What the user sees

Face detection and multi-modal video capture
The user simply looks at the camera while the system instantly captures data. It integrates depth sensors, motion data, and multiple frames to gather the most comprehensive and accurate information, without interrupting the user experience

What happens in the background

Digital spoof check
Multiple algorithms check for digital manipulation, such as deepfakes and synthetic faces. 

Physical spoof check
ML models detect physical spoofing attempts, such as paper printouts, video replays, and masks. 

Evasion check
Algorithms check for attempts to make features hard to detect, such as unnatural makeup, expressions, or blocking the face.

What the user sees

What happens in the background

Digital spoof check
Multiple algorithms check for digital manipulation, such as deepfakes and synthetic faces. 

Physical spoof check
ML models detect physical spoofing attempts, such as paper printouts, video replays, and masks. 

Evasion check
Algorithms check for attempts to make features hard to detect, such as unnatural makeup, expressions, or blocking the face.

Technical benefits

Why choose Incode’s liveness detection?

Discover how Incode Deepsight’s advanced liveness technology tackles fraud without impacting your user experience.

Full ownership of our ML models
and tech stack

Unlike other providers who rely on third-party vendors, we build and own our entire technology stack.

Our AI/ML models, powered by deep learning and designed for identity verification, allow us to train on the latest document and biometric fraud vectors. This full control enables us to tailor our models to the unique needs of our clients, ensuring superior performance and flexibility.

Rich, well-organized data
to train models

Using advanced neural networks, including both standard Convolutional Neural Networks (CNNs) and cutting-edge Large Vision Models (LVM) and transformers, we train our models to achieve state-of-the-art results across
various tasks.

Over nearly a decade, we’ve curated large, statistically representative datasets, so our models deliver balanced performance across variables like age, skin tone, and gender. Our in-house Fraud Lab has compiled over 1 million unique presentation attacks, from basic printouts to advanced
3D masks. We also generate synthetic data such as face swaps and synthetic faces, enhancing the robustness of
our models.

Internal testing for flawless detection

Our internal testing environment is designed to be more challenging than real-world spoof attempts.

By testing our models against complex attacks, we ensure perfect detection rates in production. This way, we can prevent known fraud rings, repeat verification attempts,
and other fraudulent behaviors before they impact
your business.

Leveraging multiple input modalities

Incode advanced liveness incorporates detection across various modalities, such as depth and motion, and multiple frames, multiplying its accuracy.

Full ownership of our ML models
and tech stack

Unlike other providers who rely on third-party vendors, we build and own our entire technology stack.

Our AI/ML models, powered by deep learning and designed for identity verification, allow us to train on the latest document and biometric fraud vectors. This full control enables us to tailor our models to the unique needs of our clients, ensuring superior performance and flexibility.

Rich, well-organized data
to train models

Over nearly a decade, we’ve curated large, statistically representative datasets, so our models deliver balanced performance across variables like age, skin tone, and gender. Our in-house Fraud Lab has compiled over 1 million unique presentation attacks, from basic printouts to advanced
3D masks. We also generate synthetic data such as face swaps and synthetic faces, enhancing the robustness of
our models.

Internal testing for flawless detection

Our internal testing environment is designed to be more challenging than real-world spoof attempts.

By testing our models against complex attacks, we ensure perfect detection rates in production. This way, we can prevent known fraud rings, repeat verification attempts,
and other fraudulent behaviors before they impact
your business.

Leveraging multiple input modalities

Incode advanced liveness incorporates detection across various modalities, such as depth and motion, and multiple frames, multiplying its accuracy.

How it works

Incode’s advanced
liveness technology

Within Incode’s liveness, we employ multi-modal intelligence to maximize accuracy of liveness detection without slowing down the end-user experience.

Within Incode Deepsight, we employ multi-modal intelligence to maximize accuracy of liveness detection without slowing down the end-user experience.

Other models on the market today rely on a single input, typically a 2D image. But they struggle to detect complex spoofing attempts, impacting their user experience, security, and identification accuracy.

Incode Deepsight enhances facial capture accuracy by selecting optimal frames and incorporating multiple modalities such as video, depth, and motion sensors. This approach mimics real-life interactions, reducing both false negatives and false positives.

Fraud Lab: real-time adaptation to emerging threats

Our liveness detection AI models are continuously refined by Incode’s Fraud Lab, where our team trains our proprietary AI technology in response to the latest emerging fraud techniques.

Unlike competitors who rely on third party providers, we can immediately train our in-house models to analyze input for emerging fraud types, ensuring these attacks are neutralized before they impact your business.

Liveness detection in action

Liveness detection that’s real-world ready

The Georgia Department of Driver’s Services (GA DDS)

In 2024 the GA DDS performed an independent testing of Incode’s liveness detection according to iBeta level 2 protocols. The test achieved 0 false positives (false acceptance of fraudulent users) and 0 false negatives (false rejections of genuine users).

Michigan State University

In 2023, Michigan State University’s Mobile Face Spoofing Database (MSU-MFSD) evaluated Incode’s liveness technology using a public dataset designed to test systems for spoofing attacks. The evaluation confirmed our system’s precision, achieving a 0% false positive rate and 0% false negative rate.

Global recognition