At Transmit Security, we are committed to advancing identity verification technologies that enhance security without compromising user experience. Our Presentation Attack Detection (PAD) capabilities have recently been tested and approved by iBeta, aligning with the rigorous ISO 30107-3 Biometric PAD Standard Level 1. This approval underscores our dedication to providing reliable, cutting-edge solutions that address modern security challenges.
Transmit Security and the Presentation Attack Detection (PAD)
Presentation Attack Detection (PAD) is a vital component of biometric security, designed to guard against spoofing attempts in biometrics recognition such as photos, masks or other fake representations. By analyzing and distinguishing genuine human traits from fraudulent ones, PAD ensures the authenticity of biometric data used in systems like facial recognition, fingerprint scanning and iris detection.
This technology is crucial in today’s landscape of increasingly sophisticated threats. PAD strengthens security frameworks, preventing unauthorized access and safeguarding sensitive user data. The iBeta approval affirms the effectiveness of our PAD capabilities against various spoofing techniques, tested under the stringent ISO 30107-3 Biometric PAD Standard Level 1.
Transmit Security’s two-step PAD solution
Our approved PAD approach is robust yet user-friendly, ensuring strong security without compromising ease of use:
Step 1: Intelligent preprocessing
We optimize inputs before analysis using basic machine learning algorithms:
- Eye detection: Verifying eyes are open and clearly visible.
- Occlusion detection: Identifying obstructions or coverings.
- Image quality assessment: Detecting blur or glare for better accuracy.
This preprocessing not only improves usability by guiding users to capture high-quality input but also ensures our PAD model receives reliable data.
Step 2: Advanced PAD modeling
Our model processes 2D images with capabilities that mimic the depth and precision of multi-sensor systems:
- Sensor-like functionality: Extracting depth and texture information from 2D images using advanced neural networks.
- Multi-class attack detection: Identifying various spoof types, from masks to replays, using spatial and temporal analysis.
- Feature extraction: Techniques like depth estimation, texture analysis, and reflection detection to uncover subtle signs of fraud.
This dual-layer approach ensures our PAD solution remains cutting-edge, offering both simplicity for users and advanced security to counter threats.
Path to the future of liveness verification
In the recent iBeta test, our PAD system achieved a 0% error rate. This means our technology successfully detected 100% of presentation attacks using artifacts such as print photos, paper masks and videos displayed on screens. This flawless performance is a significant milestone, underscoring the strength and reliability of our current capabilities.
However, this is just the beginning. At Transmit Security, we view this achievement as one step on the journey to creating a safer, more advanced liveness detection system. Our goal is to continuously refine and expand our capabilities to stay ahead of evolving threats and deliver unparalleled security solutions to our clients.
The road ahead
By combining our PAD technology with our innovations in biometric verification and native AI-powered services, we are committed to setting new standards in the field of identity verification — providing best-of-breed, user-friendly solutions that meet the demands of a rapidly changing digital landscape.
