Researchers from the National University of Singapore (NUS) have invented a new method of anti-counterfeiting called DeepKey. 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, jewellery, 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 secure 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 per cent accurate) authentication in less than 3.5 minutes. For further information see the IDTechEx report on Graphene Market & 2D Materials Assessment 2021-2031.
Current anti-counterfeiting technologies using PUF patterns normally face several bottlenecks, including complicated manufacturing, specialised 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."
Source and top image: National University of Singapore