用双合成参考图提升印刷防伪图案的跨摄像头认证可靠性
Authentication of Copy Detection Patterns via Cross-Camera Dual-Synthetic Referencing
- 通过双合成参考：结合模板与实拍图像生成更精准验证基准
- 在异构手机相机下认证准确率显著提升，小区域也能可靠识别
- 对机器学习伪造攻击有强防御力，适合低配设备部署
复制检测模式（CDPs）是印在实物上的结构化图案，用于低成本防伪认证。验证时需将拍摄图像与打印所用数字模板比对。但打印机随机性和相机畸变会干扰比对，降低抗伪造能力。现有方法仅通过合成验证相机域图像缓解相机影响，却忽略打印差异。本文提出基于注册的跨摄像头双合成参考框架：先用受控相机采集每张印刷的CDP，再用深度学习翻译器结合数字模板与注册图像，生成高质量验证参考图。理论分析表明，双重参考比仅依赖模板更具信息量。实验在异构移动相机上验证，性能提升显著，对机器学习伪造攻击具备鲁棒性，可在小面积区域和低端设备上稳定工作。
原文摘要 · Abstract (English)
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. Experiments on heterogeneous mobile cameras demonstrate improved authentication performance, robustness to machine-learning-based copy attacks, and reliable verification from small CDP regions and on low-end devices.
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