Biometric Vein Recognition Hacked

Biometric vein recognition is considered with iris recognition as the most secure biometrics system. Vein recognition is used in highly secure areas. Automatic Telling Machines starts to use this technology with, for instance in Japan. This statement was valid until December 2018. At the famous German Chaos Communication Congress (35c3), Krissler Ian, also known as Starbug, and Albrecht Julian demonstrated a method (German video) to create a lure hand that defeats commercial systems.

Starbug is a well known hacker in the field of biometrics. For instance, in 2016, he faked successfully the fingerprints of a German minister using high resolution captured photos.

For about 20 years, vein recognition is mainly a Japanese technology. Fujitsu and Hitachi are the two leaders. The network of veins is captured either by reflection from the palm or through transparency with Infra Red (IR) light for fingers. The captured network is turned into minutiae like a typical fingerprint.

The capture phase seems rather simple. The researchers removed the IR filter of a traditional high-end DSLR camera (in that case, Nikon D600) with good lenses. They were able to get a proper capture up to 6 meters with a flash. They also built a raspberry-based system that could be hidden into a device, for instance, a hand-dry-blower. The captured image is processed via a python script to generate a skeleton of the network of veins (as illustrated by the figure below).

Once the skeleton available, they build a fake hand (or finger) using bee wax. The fake hand covers the printed picture. They tried many different materials, but the wax presented the best performance concerning transparency and diffraction of IR light, in other words, it better emulated skin.

Once the fake hand available, the attacker has to use it on the detector. They performed a live demonstration. The demonstration highlighted that the lighting conditions were critical. The strong lighting of the scene spoiled the demonstration, and they had to shade the detector to success. On the other hand, the fake finger detection went on smoothly. The detector was a kind of tunnel. At the time of the presentation, Hitachi and Fujitsu did not have yet reacted.

The attacked detectors had no liveliness detection. As I highlighted in section 7.4.2 of “Ten Laws for Security,” detecting the presence of a real living being behind the captured biometrics is necessarily for robust systems. Unfortunately, such detection increases the complexity and cost of detectors.

Conclusion: Once more, Law 1: Attackers will always find their way
was demonstrated.

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