Authentication of Copy Detection Patterns via Cross-Camera Dual-Synthetic Referencing 文章

arXiv:2605.31292v1 Announce Type: new Abstract: Copy Detection Patterns (CDPs) are structures printed on physical objects to enable cost-effective authentication. Verification is achieved by comparing a captured image with the digital template from which the CDP was printed. In practice, printer stochasticity and camera distortions hinder this comparison, limiting robustness against counterfeiting. Prior work addressed camera effects by synthesising reference images in the verification camera domain, but it ignored printing variability. We introduce an enrolment-based cross-camera dual-synthetic referencing framework. Each printed CDP is first captured by a controlled enrolment camera, and a deep-learning-based translator jointly exploits the digital template and the enrolled capture to generate a high-quality reference for the verification image. We provide an information-theoretic justification showing that the dual reference is more informative than template-based references.