为防伪图案提供可证明的性能保障，突破传统评估局限
Provable Performance Guarantees of Copy Detection Patterns
- 构建理论框架，推导最优检测标准
- 克服传统方法对干扰敏感、泛化差的问题
- 适合安全认证、防伪技术研究者参考
复制检测图案（CDPs）在食品、医药、化妆品等领域的安全防护中至关重要。当前对CDPs的性能评估多依赖哈明距离或皮尔逊相关性等简单指标，这些方法易受打印与成像过程中的失真和统计变化影响，且难以泛化至未见的伪造样本。基于机器学习的方法也存在分布偏移问题。鉴于其在防范造假（如新冠疫情期间假疫苗）中的关键作用，亟需建立可证明的性能保证体系。本文旨在构建理论框架，推导出适用于分析、优化与未来发展的最优评估准则，确保CDPs在多样安全场景下的可靠性与有效性。
原文摘要 · Abstract (English)
Copy Detection Patterns (CDPs) are crucial elements in modern security applications, playing a vital role in safeguarding industries such as food, pharmaceuticals, and cosmetics. Current performance evaluations of CDPs predominantly rely on empirical setups using simplistic metrics like Hamming distances or Pearson correlation. These methods are often inadequate due to their sensitivity to distortions, degradation, and their limitations to stationary statistics of printing and imaging. Additionally, machine learning-based approaches suffer from distribution biases and fail to generalize to unseen counterfeit samples. Given the critical importance of CDPs in preventing counterfeiting, including the counterfeit vaccines issue highlighted during the COVID-19 pandemic, there is an urgent need for provable performance guarantees across various criteria. This paper aims to establish a theoretical framework to derive optimal criteria for the analysis, optimization, and future development of CDP authentication technologies, ensuring their reliability and effectiveness in diverse security scenarios.
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