The increasingly pervasive problem of counterfeiting affects both individuals and industry.In particular, public health and medical fields face threats to device authenticity and patient privacy, especially in the post-pandemic era.Physical unclonable functions (PUFs) present a modern solution using copyright-proof security labels to securely authenticate and identify physical objects.
PUFs harness innately entropic information generators to create a unique fingerprint for an authentication protocol.This paper proposes a facile protein self-assembly process as an entropy generator for a unique biological PUF.The Picnic Basket posited image digitization process applies a deep learning model to extract a feature vector from the self-assembly image.
This is then binarized and debiased to produce a cryptographic key.The NIST SP 800-22 Statistical Test Suite was used to evaluate the randomness of the generated keys, which proved sufficiently stochastic.To facilitate deployment on physical objects, the PUF images were printed on flexible silk-fibroin-based biodegradable labels using functional protein Translator Circuit Board bioinks.
Images from the labels were captured using a cellphone camera and referenced against the source image for error rate comparison.The deep-learning-based biological PUF has potential as a low-cost, scalable, highly randomized strategy for anti-counterfeiting technology.