Collaborative Research: Facilitating Supply Chain Trust via Micro-Surface Sensing and Vision-Enabled Authentication

合作研究：通过微表面传感和视觉认证促进供应链信任

• 批准号: 2227499
• 负责人: Chau-Wai Wong
• 金额: $ 30万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2227499&HistoricalAwards=false
• 关键词: Collaborative; Research; Facilitating; Supply; Chain

Counterfeit products prevail due to untrusted supply chains and have been inflicting significant harm to our society at all scales, from public health, to the economy, and to national security. Distributed ledger technologies such as blockchain have recently been applied to provide unforgeable digital records and to improve the trust of the global supply chains. Despite the improved trust in cyberspace, duplication attacks in the physical world are still possible, making counterfeits remain a serious concern, especially for products of high value. Commonly used QR codes or low-cost RFID tags do not prevent counterfeiters from linking fake goods to the distributed-ledger-based supply chain in cyberspace, which compromises the trust of the overall supply chain. To address this vulnerability, we propose establishing a secure cyber-physical link using physically unclonable features (PUFs) of an item’s surface at the microscopic scale. These microscopic features constitute a “fingerprint,” which can uniquely characterize and identify the specific item being protected and can be captured by consumer-grade cameras. This research project focuses on microsurface sensing and vision-based authentication and offers a novel solution to complement the existing blockchain-based countermeasures in supply chain management. This non-forgeable cyber-physical link provides proactive authentication by supply chain participants/end-users. Successful research in this area can mitigate the illicit reuse of old electronic components and prevent counterfeit medicines from reaching patients.The proposed research effort aims at investigating the scientific foundation and overcoming technological barriers that utilize surface PUFs for linking the physical world to supply chains securely and uniquely via ubiquitous mobile imaging. First, surface PUFs in mobile imaging settings are examined to uniquely identify objects, with a focus on designing efficient and scalable computer vision algorithms. Second, microscopic-level light reflection models are established via physics-guided signal analysis and machine learning approaches to extract surface PUF signals. Third, the integration of the surface PUF verification with the blockchain-based supply-chain information management system is explored and multiscale data collections are planned. The proposed research is expected to advance the scientific understanding of photometric behaviors of microstructures and result in more accurate light reflection models at the microscopic scale via both controlled experiments and machine learning. The information-theoretic modeling of the PUF’s inherent randomness and channel randomness due to processing steps can potentially help identify the bottleneck of the verification system and improve its performance. The proposed ambient light decomposition and estimation scheme facilitate a capability leaping of computer vision for authenticating scenes at the microscopic scale. The proposed research program has the potential to protect public health and national security via safeguarding supply chains. The ubiquity of mobile devices multiplies the economic and societal impacts of our research. The proposed program also seamlessly integrates research, education, and outreach. It will prepare the future security-aware workforce by fascinating them with cutting-edge research results via lectures, projects, and global competitions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于供应链不可信，假冒产品盛行，并对我们社会的各个层面（从公共卫生到经济和国家安全）造成了重大损害。区块链等分布式账本技术最近被应用于提供不可伪造的数字记录，并提高全球供应链的信任度。尽管网络空间的信任度有所提高，但物理世界中的复制攻击仍有可能发生，这使得假冒产品仍然是一个严重的问题，特别是对于高价值的产品。常用的QR码或低成本的RFID标签并不能阻止造假者将假货与网络空间中基于分布式账本的供应链联系起来，这会损害整个供应链的信任。 为了解决这个漏洞，我们建议建立一个安全的网络物理链接，使用物理不可克隆的功能（PUF）的项目的表面在微观尺度。这些微观特征构成了“指纹”，它可以唯一地表征和识别被保护的特定物品，并且可以被消费级相机捕获。该研究项目的重点是微表面传感和基于视觉的身份验证，并提供了一种新的解决方案，以补充供应链管理中现有的基于区块链的对策。这种不可伪造的网络物理链接为供应链参与者/最终用户提供了主动认证。 该领域的成功研究可以减少旧电子元件的非法再利用，并防止假冒药品进入患者手中。拟议的研究工作旨在调查科学基础，并克服技术障碍，利用表面PUF通过无处不在的移动的成像将物理世界与供应链安全和独特地联系起来。首先，检查移动的成像设置中的表面PUF以唯一地识别对象，重点在于设计高效且可扩展的计算机视觉算法。其次，通过物理引导的信号分析和机器学习方法建立微观水平的光反射模型，以提取表面PUF信号。第三，探索表面PUF验证与基于区块链的供应链信息管理系统的集成，并计划进行多尺度数据收集。该研究有望推进对微结构光度学行为的科学理解，并通过受控实验和机器学习在微观尺度上建立更准确的光反射模型。由于处理步骤，PUF的固有随机性和信道随机性的信息理论建模可以潜在地帮助识别验证系统的瓶颈并提高其性能。所提出的环境光分解和估计方案促进了计算机视觉在微观尺度上认证场景的能力飞跃。拟议的研究计划有可能通过保护供应链来保护公共健康和国家安全。移动的设备的普及使我们研究的经济和社会影响成倍增加。拟议的计划还无缝集成研究，教育和推广。该奖项旨在通过讲座、项目和全球竞赛等形式，向未来的安全意识工作者展示前沿研究成果，为他们做好准备。该奖项体现了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。