Fast authentication using AI deep learning algorithms

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 “search and compare” 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.

“With 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,” 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. “We 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,” 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 team’s 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 researchers’ fabrication technique is significantly faster and safer.

Next steps

The NUS team has filed a patent for their invention and is now planning to push this technology one step further. “We are searching for better, faster, and more robust readout and authentication approaches for our PUF tags,” said Asst Prof Wang.

The team has already begun to conduct research on other readout techniques to further shorten the processing time. “In 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,” Asst Prof Wang added.