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Consumables—from food to pharmaceuticals and supplements—are becoming increasingly vulnerable to various modes of counterfeiting due to the growing complexity of their supply chain. Mislabeling, re-branding, and false advertising are prevalent in this sector. Existing physical authentication techniques fail to adequately verify integrity of these products and protect the end-users. In this paper, we aim at addressing this critical problem through the development of a novel authentication solution. It builds on the chemical analysis properties of a powerful spectroscopy technique, namely, nuclear quadrupole resonance (NQR) that is quantitative, non-invasive, low-cost, and amenable for miniaturization (to hand-held form factors). The method is sensitive to small variations in the solid-state chemical structure of a sample, which change the NQR signal properties. These attributes can be unique for various manufacturers, enabling their use as manufacturer-specific watermarks. However, NQR spectroscopy only works reliably (i.e., provides good sensitivity) on compounds that contain certain nuclear isotopes. We take advantage of the intrinsic properties of NQR-sensitive isotopes to use them as extrinsic tags in NQR-insensitive products. The NQR spectra of these extrinsic tags act as unique watermarks that can be analyzed using machine learning methods to authenticate any consumable with high confidence. In particular, we use support vector machines to classify the measured spectra and confirm the identity of items under test. We have assessed this approach on a variety of consumables utilizing semi-custom equipment and verified that it results in high (>95%) classification accuracy. In order to prove the unclonability of such extrinsic tags, we have also performed a mathematical analysis that proves the randomness of the extrinsic tag and confirms its robustness to brute-force attacks.

Robust Authentication of Consumables With Extrinsic Tags and Chemical Fingerprinting