2D tags
Context: A new anti-counterfeiting technique uses two dimensional (2D)-material tags along with artificial intelligence (AI)-driven authentication software, and promises to deliver faster, more accurate results even under extreme conditions.
DeepKey
- The new method called ‘DeepKey’ was developed by an international team of researchers, led by the National University of Singapore (NUS).
- The team detailed their work in a study titled ‘Multigenerational Crumpling of 2D Materials for Anti-counterfeiting Patterns with Deep Learning Authentication’, published in the scientific journal Matter.
- The 2D-material secure tags have randomly generated ‘Physically Unclonable Function’(PUF) patterns, which can be categorised and validated by a deep learning model.
- The authentication process takes under 3.5 minutes to complete, and involves scanning the tags under an electronic microscope to obtain the PUF pattern, which is sent to the AI-driven software for validation.
- 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 new technology can be used with valuable products such as jewellery, and electronics as it “reaches nearly 100% validation precision.”
- Also, the tags can be applied on COVID-19 vaccines for authentication, including the ones that are stored at very low temperatures.
- PUF key-based technologies generally offer high encoding capabilities as they can be used to produce numerous dissimilar patterns.
- Although, it makes the pattern authentication process longer, when performed within a large database.
- The deep learning model is used to pre-categorise the PUF patterns into subgroups, and so the search-and-compare algorithm is conducted in a much smaller database, which shortens the overall authentication time.