Assistant Professor Wang Xiaonan (left) and Dr Jing Lin (right),
together with their research team in NUS Engineering, have invented
DeepKey, an anti-counterfeiting technology for reliable AI
authentication under extreme conditions.
Researchers from the National University of Singapore (NUS) have
invented a new method of anti-counterfeiting calledDeepKey.
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 -70Â°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 teamâs 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.
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.
âWith this research, we have tackled several bottlenecks that
other techniques encounter,â said Asst Prof Wang. âOur
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
The researchers published their results in scientific
journal Matter on 2
December 2020. This study was conducted in collaboration with
researchers from Anhui University of Technology and Nanyang
A stable, simple, and scalable process to create PUF
Remarkably, the researchers do not need any special equipment to
create the secure tags. They can simply be made with a balloon, a
bottle of 2D-material dispersion, and a brush.
âFirst, 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 afterwards, and normally, hundreds of them can
be made at one time,â said Dr Jing Lin, a member of the research
Next, the researchers take a quick image of the PUF tag with an
electron microscope, which is then synced to their innovative
software to go through the deep learning âclassification and
validationâ process. âThe 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 very fast, in
less than 20 seconds,â explained Dr Jing.
Fast authentication using AI deep learning
All PUF key-based technologies have ultra-high encoding
capacities because of the huge numbers of distinct patterns that
can be theoretically produced. However, the high encoding capacity
also leads to long authentication time, as the âsearch and
compareâ pattern validation has to be conducted within a huge
database. This trade-off between high encoding capacity and long
authentication time often restricts such PUF-based
anti-counterfeiting tags from practical applications.
âWith our new technology, we are breaking this long-lasting
trade-off between high encoding capacity and long authentication
time by using classifiable 2D-material PUF tags and deep learning
algorithms,â said Asst Prof Wang.
First, the researchers used various 2D materials to fabricate
PUF tags with AI recognisable features. Second, they trained a deep
learning model to conduct a two-step authentication mechanism.
âWe used the deep learning model 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,â Asst Prof Wang explained.
Currently, the only available technologies similar to this NUS
innovation, are polymer wrinkle-based tags. Wrinkled polymer tags
are authenticated based on the surface patterns just like the novel
2D-material tags. However, their authentication presently requires
one-by-one feature extraction and matching, which is slow and shows
only 80 per cent reliability. The NUS teamâs authentication is
boosted by deep learning, and is therefore much faster, and reaches
nearly 100 per cent validation precision.
In addition, compared to the wet chemistry preparation of
polymer wrinkle-based tags, which involves the use of harmful
organic chemicals and UV light, the NUS researchersâ fabrication
technique is significantly faster and safer.
The NUS team has filed a patent for their invention and is now
planning to push this technology one step further. âWe are
searching for better, faster, and more robust readout and
authentication approaches for our PUF tags,â said Asst
The team has already begun to conduct research on other readout
techniques to further shorten the processing time. âIn addition,
such naturally encoded information by the PUF tags could be further
secured by being kept on blockchain, so that the whole supply chain
and quality control can be transparently tracked,â Asst Prof Wang