Woodcoin authentication relies on the physical uniqueness of natural woodgrain combined with decentralized photographic records. Each coin consists of a machined wooden blank engraved on one face with three elements: (1) denomination, (2) an 8-character alphanumeric identifier, and (3) date of issue. The reverse face is unmarked except for a single engraved reference dot located near the perimeter. At minting, high-resolution photographs of both faces are taken with the reference dot positioned at the 12 o’clock orientation. These images are stored in a decentralized distribution channel, such as a global BitTorrent network, and may also be mirrored in private or institutional databases.
Verification proceeds in two stages: metadata match and grain pattern match. First, the verifier queries the registry using the coin’s alphanumeric code and date to retrieve the corresponding reference images. Second, the coin is physically oriented so the reference dot is at 12 o’clock and the woodgrain pattern on the reverse face is visually compared to the authenticated image. Since woodgrain patterns are non-reproducible even with advanced manufacturing, the probability of a false positive match is negligible. This process can be executed manually or automated using computer vision. Machine learning models trained on high-resolution coin imagery can detect grain feature alignment, rotation normalization, and microstructural anomalies. Such algorithms can be integrated into mobile applications, point-of-sale (POS) devices, or ATM-style dispensers, enabling rapid in-field verification and counterfeit rejection without reliance on a central authority.
