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: 2227261
• 负责人: Min Wu
• 金额: $ 15万
• 依托单位: University of Maryland, College Park
• 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=2227261&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.

假冒产品由于不可信的供应链而盛行，并在各个层面上对我们的社会造成了重大损害，从公共卫生到经济，再到国家安全。区块链等分布式账本技术最近已被应用于提供不可伪造的数字记录，并改善全球供应链的信任。尽管网络空间的信任得到了改善，但现实世界中的复制攻击仍然是可能的，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。常用的二维码或低成本的RFID标签并不能阻止造假者将假货与基于分布式账本的网络空间供应链联系起来，这损害了整个供应链的信任。为了解决这一漏洞，我们建议在微观尺度上使用物品表面的物理不可克隆特征（puf）建立安全的网络物理链接。这些微观特征构成了一个“指纹”，它可以独特地表征和识别被保护的特定物品，并可以被消费级相机捕捉到。该研究项目专注于微表面传感和基于视觉的认证，并提供了一种新的解决方案，以补充现有的基于区块链的供应链管理对策。这种不可伪造的网络物理链路为供应链参与者/最终用户提供了主动认证。这一领域的成功研究可以减少旧电子元件的非法再利用，并防止假冒药品到达患者手中。拟议的研究工作旨在调查利用表面puf的科学基础和克服技术障碍，通过无处不在的移动成像将物理世界与供应链安全而独特地连接起来。首先，研究了移动成像设置中的表面puf，以唯一地识别物体，重点是设计高效和可扩展的计算机视觉算法。其次，通过物理引导信号分析和机器学习方法建立微观级光反射模型，提取表面PUF信号。第三，探索地表PUF验证与基于区块链的供应链信息管理系统的集成，规划多尺度数据采集。该研究有望促进对微观结构光度行为的科学理解，并通过控制实验和机器学习在微观尺度上产生更精确的光反射模型。对PUF固有的随机性和由于处理步骤而产生的信道随机性进行信息论建模，可以潜在地帮助识别验证系统的瓶颈并提高其性能。本文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对场景进行验证的能力飞跃。拟议的研究项目有可能通过保护供应链来保护公众健康和国家安全。无处不在的移动设备成倍增加了我们研究的经济和社会影响。该计划还将研究、教育和推广无缝地整合在一起。将通过讲座、项目、全球竞争等，吸引尖端研究成果，培养具有未来安保意识的人才。该奖项反映了美国国家科学基金会的法定使命，并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。