This invention discloses a method,
system,
computer equipment, and readable storage medium for signature image
DNA encoding storage and anti-counterfeiting
verification based on
feature learning, belonging to the field of
electronic information technology. The method includes: extracting high-dimensional discriminative feature vectors from the signature image to be verified using a feature extractor; inputting the vectors into a trained sequence
encoder to generate
a DNA sequence; synthesizing a reverse complementary sequence to prepare a labeled probe; hybridizing the probe with the registered
DNA sequence of the target signer; measuring the hybridization yield; if the yield exceeds a preset threshold, the signature is determined to be genuine; otherwise, it is a forged signature. This invention is the first to integrate handwritten signature anti-counterfeiting with
DNA storage. Through three core components—a feature extractor, a sequence
encoder, and a hybridization predictor—and multi-task joint training, genuine signature pairs are encoded as high-hybridization-yield sequences, while genuine and forged signature pairs are encoded as low-hybridization-yield sequences, achieving physical-level anti-counterfeiting
verification based on
molecular hybridization. The
verification process of this invention is inherently parallel, energy-efficient, and highly accurate, meeting the needs of practical anti-counterfeiting applications.