Face Liveness Detection
Anti-spoofing Presentation Attack Detection (PAD)
BioID’s liveness detection software prevents spoofing in both presentation attacks and deepfakes. It builds trust in online transactions by ensuring that the person authenticating is real and physically present in front of the camera.
How does Liveness Detector work?
Facial Liveness Detection
A unique fusion technology
1
3D Object Validation
By capturing only two images using any standard camera, BioID’s unique motion-analysis algorithm validates whether the face detected is indeed from a real person.
2
AI Technology
Powerful DNNs (deep neural networks) are used to detect presentation attacks like 3D masks, video replays, projections, etc.
3
Deepfake Check
Sophisticated algorithms are incorporated to reject images/videos that have been manipulated using deepfake tools.
4
Challenge-Response
Optional patented user interaction to obtain ‘user consent’ during the video liveness check.
Liveness Verification for Face Recognition
There are various ways to detect presentation attacks in biometrics. Hardware-dependent techniques use 3D cameras to look for depth information from a 3D face or infrared cameras to detect thermal information. However, both require special equipment and are therefore not compatible with most webcams and mobile phone cameras available today. Instead, the BioID liveness detection algorithm works camera-independently.
Besides BioID’s challenge-response patent from 2004, one of the first solutions on the market was eye blinking detection, measuring intrinsic facial movement. This seems reasonable; after all, a photo cannot blink. Or can it? An attacker can simply take a photo, cut out holes for the eyes, hold it in front of their face, and blink. If done carefully, this can fool many blink detection systems. A video of the person blinking would also work.
Additionally, these systems are inconvenient for users as they take a comparably long time to check if a real and alive person is presenting themselves. Some mechanisms look for pupil dilation, for instance, by making the screen dark and then suddenly bright. This can successfully detect fakes but is also vulnerable to a photo with eye holes or a well-timed video.
Thus, a combination of techniques is ideally the most reasonable approach to coping with different (deepfake) attack scenarios. Today, BioID combines traditional approaches from more than 20 years of experience with the latest AI deep convolutional neural networks (DCNNs).
Detect being Live in Various Ways
Active Liveness Detection for Enhanced Security
BioID’s technology, compliant with
ISO 30107 standards, provides robust protection against security threats such as replay attacks and deepfake fraud. By incorporating simple, user-friendly gestures like nodding, our biometric authentication system ensures that only live, genuine individuals can access your secure platforms. This advanced identity verification method significantly reduces the risk of fraud, making it an essential component of any security strategy.
Passive Liveness Detection for Effective Authentication
This provides a seamless fraud prevention solution that requires no active participation from users. Integrated with deepfake detection, this technology silently analyzes subtle indicators to confirm a user’s authenticity, ensuring secure access without disrupting the user experience. This advanced method of biometric authentication protects against fraudulent activities while maintaining effortless security.
Video Live Detection for Authenticity Verification
This BioID technology accurately verifies whether a video was recorded in real-time or not. By analyzing uploaded video files, this advanced system distinguishes between live footage and pre-recorded content, providing essential protection against video-based fraud and deepfakes. Integrate this tool into your security processes to ensure the authenticity of video submissions and safeguard your platform from deceptive practices.
Try out the demo on BioID’s Playground or the API by requesting a trial instance.
Effective Mechanism against Spoofing Attacks
BioID Web Service (BWS) aims to generate the same trust and user experience as face-to-face interaction. As a result, a series of liveness verification mechanisms for anti-spoofing were developed and introduced as early as 2009. The essence is to make sure submitted recordings were indeed taken from a live person in front of the camera.
The latest mechanism for Presentation Attack Detection (PAD) prevents forgery through replay attacks like videos, recorded deepfakes, or avatars. It is based on texture detection and AI.
By means of texture analysis, the texture of a recaptured image, a video, a projection, or other fake attempts is now detected for reliable biometric anti-spoofing. Today, PAD detects even remote-controlled 3D avatars, deepfakes, as well as 3D masks. BioID has earned another patent for its liveness detection technology.
It is based on an optical flow algorithm to discern between a 2D and a 3D object. By means of a simple but sensitive motion trigger, the algorithm captures images automatically, preventing an attacker from presenting or swapping different photos.
Detect Deepfakes with Liveness Detection
Our PAD goes beyond simple identity verification by ensuring that the person interacting with the system is a live, genuine individual. Our technology analyzes various biometric signals — such as facial movements, eye blinks, and other subtle indicators of life — while simultaneously detecting signs of manipulation typical in deepfake content.
By integrating the BioID Deepfake Detector into our liveness detection framework, we provide a robust solution that enhances security and maintains trust in online environments.
With BioID’s approach, users can confidently engage in digital interactions, knowing that our technology effectively protects against evolving deepfake threats.
FAQs
What is Liveness Detection?
Typically referred to as Presentation Attack Detection or PAD, it validates whether a user is physically present in front of the camera. It is a crucial component in the fight against deepfakes and fraudulent identity authentication. It distinguishes live persons from spoofing attacks by means of presenting to the camera a photo/video of a person or impersonating a person using a face mask without the physical presence of the impersonated person. It is a software-based technology generally used in conjunction with face recognition.
Why Liveness Detection?
It is needed to secure an online transaction from spoofing attacks, such as deepfakes. For instance, a fraudster could use a photo, video, or mask of a legitimated person to spoof a facial recognition system and gain unauthorized access to accounts or data.
How does it work?
It is designed to effectively detect and prevent presentation attacks, like deepfakes. Image processing algorithms analyze images or videos and decide whether they were captured by a live person. Software-based technologies include motion analysis, texture analysis, artificial intelligence (AI), or a combination of the above. Hardware-based solutions are typically those relying on the use of a 3D camera and/or multiple cameras.
What is a Challenge-Response?
A challenge-response system requires the user to correctly respond to a specific action prompted by the ‘challenge’. A challenge-response system is the most effective means to ensure user consent protecting both system integrity and data privacy. In a typical face application with challenge-response, the user is prompted to turn the head in one or more random directions. Only when the requested head directions were followed by the user correctly, the challenge-response is considered successful. The more challenges used, the higher the level of security, as it is more challenging for an attacker to have a video or deepfake recording showing exactly these head movements.
Active or Passive?
The active analysis requires the user to react in a certain way. In particular, it requires a user to intentionally confirm his or her presence by interacting with the system (e.g., by nodding) and is therefore particularly useful for applications requiring user consent, putting high importance on data privacy. For fighting attacks at the application level, e.g. through the injection of modified camera streams, challenge-response mechanisms can be utilized to reject prerecorded videos/deepfakes.
The passive approach does not require any specific actions from the user. Therefore, it is possible to perform passive LD without the user’s interaction by focusing on usability.
ISO/IEC 30107-3 compliance
Confirmed by TÜViT
BioID’s face liveness verification is compliant with industry standards, as confirmed by two independent FIDO-accredited testing laboratories:
- ISO/IEC 30107-3: 2017 Presentation Attack Detection (PAD) Levels 1 & 2 compliant as certified by TÜV Informationstechnik GmbH (TÜViT) in Germany.
- FIDO ISO 19795 and ISO/IEC 30107 Biometric Component as certified by laboratories ELITT/Leti in France. BioID’s anti-spoofing technology is a vital part of the standard specifications for fraud prevention, including deepfake detection.
- BioID’s certified liveness detection is compliant with the standard for Remote Identity Verification Providers (PVID) as published by ANSSI (The National Information Systems Security Agency). In 2022, BioID has been tested successfully as part of a PVID certified identity verification solution.
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Made in Germany
since 1998
Originating from the research institute Fraunhofer IIS in 1998, BioID is a German biometrics company.
Our technology has a proven record since its inception and is trusted by countless enterprises, banks, and government organizations.