Researchers from the National University of Singapore (NUS) have invented a new anti-counterfeiting method called DeepKey. It is developed in just eight months; this security innovation uses two-dimensional (2D)-material tags and artificial intelligence (AI)-enabled authentication software.
Compared to conventional anti-counterfeiting technologies, DeepKey works faster, achieves highly accurate results, and uses durable identification tags that are not easily damaged by environmental conditions such as extreme temperatures, chemical spills, UV exposure, and moisture. This new authentication technology can be applied to different high-value products, ranging from drugs, jewelry, and electronics. For example, DeepKey is suitable for tagging COVID-19 vaccines to enable rapid and reliable authentication, as some of such vaccines need to be stored at the ultra-cold temperature of -70°C.
Led by Asst Prof Chen Po-Yen and Asst Prof Wang Xiaonan from the Department of Chemical and Biomolecular Engineering at NUS Faculty of Engineering, the team’s 2D-material certain tags exhibit Physically Unclonable Function patterns (PUF patterns), which are randomly generated by systematically crumpling the 2D-material thin films. The complex 2D-material patterns with multi-scale features can then be classified and validated by a well-trained deep learning model, enabling reliable (100 percent accurate) authentication in less than 3.5 minutes.
Current anti-counterfeiting technologies using PUF patterns commonly face several bottlenecks, including complicated manufacturing, specialized and tedious readout process, long authentication time, insufficient environmental stability, as well as being costly to make.
“With this research, we have tackled several bottlenecks that other techniques encounter,” said Asst Prof Wang. “Our 2D-material PUF tags are environmentally stable, easy to read, simple, and inexpensive to make. In particular, the adoption of deep learning accelerated the overall authentication significantly, pushing our invention one step further to practical application.”
The researchers published their results in the scientific journal Matter on 2 December 2020. This study was conducted in collaboration with researchers from the Anhui University of Technology and Nanyang Technological University.
A stable, simple, and scalable process to create PUF tags
Remarkably, the researchers do not need any special equipment to complete the secure tags. They can be made with a balloon, a bottle of 2D-material dispersion, and a brush.
“First, we inflate the balloon and brush over its surface with viscous 2D-material ink. After air-drying overnight, we deflate the balloon. Because of the interfacial mechanical mismatch between the 2D-material and latex substrate, large-area, crumpled PUF patterns are generated during the contraction. These PUF patterns can be cut to the required size afterward, and normally, hundreds of them can be made at one time,” said Dr. Jing Lin, a member of the research team.
Next, the researchers take a sharp image of the PUF tag with an electron microscope, which is then synced to their innovative software to go through the deep learning “classification and validation” process. “The whole process takes less than 3.5 minutes, most of which is spent waiting for the readout from the electron microscope. The authentication itself is speedy, in less than 20 seconds,” explained Dr. Jing.