RI: Small: A Computational Framework for Marking Physical Objects against Counterfeiting and Tampering

RI：小型：用于标记物理对象防伪和篡改的计算框架

• 批准号: 0913875
• 负责人: Daniel Aliaga
• 金额: $ 49.99万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0913875&HistoricalAwards=false
• 关键词: RI; Small; Computational; Framework; Marking

The project?s goal is to create a science for embedding information in physical objects whose manufacturing process is inherently imprecise. The team will reach their objective through the investigation of a specific problem of significant economic importance: thwarting counterfeiting, and the related problem of physical tamper-detection. Counterfeiting is a growing economic problem that has been called the ?crime of the century? by a recent manufacturing industry report, and its cost is rapidly escalating (its yearly cost to the automotive industry alone is in the tens of billions of dollars and the loss of about 250,000 U.S. jobs for the legitimate manufacturers). In terms of scientific impact, the project has excellent potential for launching a significant new field. While the marking of digital objects is a well-explored area, the creation of algorithms for placing marks in physical objects is mostly unexplored territory. In terms of industrial impact, the project also has excellent potential for profoundly improving the current ?state of the practice?, which is all too easily defeated by sophisticated counterfeiters. This is because the project?s framework assumes an adversary with powerful capabilities, such as greater manufacturing prowess than the legitimate manufacturer, and full knowledge of the algorithms used to embed marks and to read marks (i.e., no ?security through obscurity?). The project?s only assumption is that the adversary does not know a secret key used to embed the mark. The work focuses on the development of the computational algorithms necessary to resolve several difficult issues and tradeoffs about what information to embed in the object, and where/how to embed it. A solution must not increase manufacturing cost and must be usable with the current manufacturing pipeline. The approach is inherently multidisciplinary, combining information hiding, computer vision/graphics, and robust algorithms. Hence, students in the project will acquire a unique combination of skills.

该项目的目标是创建一门科学，将信息嵌入到制造过程本质上不精确的物理对象中。该团队将通过调查具有重大经济意义的具体问题来实现其目标：阻止伪造以及物理篡改检测的相关问题。假冒伪劣是一个日益严重的经济问题，被称为“世纪犯罪”。根据最近的一份制造业报告，其成本正在迅速上升（仅汽车行业每年的成本就达数百亿美元，合法制造商在美国损失了约 25 万个工作岗位）。就科学影响而言，该项目具有开创重要新领域的巨大潜力。虽然数字对象的标记是一个被充分探索的领域，但用于在物理对象上放置标记的算法的创建大多是未经探索的领域。就行业影响而言，该项目还具有深刻改善当前“实践状况”的巨大潜力，而这种状况很容易被老练的造假者击败。 这是因为该项目的框架假设对手具有强大的能力，例如比合法制造商具有更强的制造能力，并且完全了解用于嵌入标记和读取标记的算法（即，没有“通过模糊实现安全性”）。该项目的唯一假设是对手不知道用于嵌入标记的密钥。这项工作的重点是开发必要的计算算法，以解决一些困难问题和权衡有关在对象中嵌入哪些信息以及在何处/如何嵌入信息的问题。解决方案不得增加制造成本，并且必须可用于当前的制造流程。该方法本质上是多学科的，结合了信息隐藏、计算机视觉/图形和强大的算法。因此，该项目的学生将获得独特的技能组合。