SaTC: CORE: Small: Forensic Coding: Robust Information Embedding in 3D Printed Objects.

SaTC：核心：小型：取证编码：在 3D 打印对象中嵌入强大的信息。

• 批准号: 2223032
• 负责人: Netanel Raviv
• 金额: $ 44.33万
• 依托单位: Washington 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=2223032&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Forensic; Coding

3D printing brings about a revolution in consumption and distribution of goods, but poses a significant risk to public safety. Any individual with internet access and a commodity printer can now produce untraceable firearms (ghost guns), keys, and dangerous counterfeit products. To combat these threats, it is important to embed identifying information inside the object, so that the perpetrator could be identified if the object is confiscated or left at a crime scene. Embedding information in a 3D printed object is a relatively new area, and various engineering solutions have been proposed as of late. Yet, for security applications, information embedding techniques must be resilient to adversarial tampering, so that the identifying information could be extracted even if the object was intentionally damaged. To this end, this project focuses on developing robust methods for embedding information inside 3D printed objects using the theory of error correcting codes.Correcting adversarially induced errors is a well-studied topic in coding theory, and many good codes exist. However, since adversaries can physically tamper with a 3D object, 3D printed information is prone to unique noise patterns that are not addressed by existing codes. In the proposed work, these patterns will be analyzed and abstracted mathematically, and new codes will be developed to combat any limited adversary. The project is expected to develop practical mechanisms for worst-case robustness in 3D information embedding, as well as new coding-theoretic concepts, and experimental work at the proof-of-concept level.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.

3D打印带来了商品消费和分销的革命，但对公共安全构成了重大风险。现在，任何拥有互联网接入和商品打印机的个人都可以生产无法追踪的枪支（幽灵枪），钥匙和危险的假冒产品。为了打击这些威胁，必须在物品内嵌入识别信息，以便在物品被没收或留在犯罪现场时能够识别犯罪人。在3D打印物体中嵌入信息是一个相对较新的领域，最近已经提出了各种工程解决方案。然而，对于安全应用，信息嵌入技术必须对对抗性篡改具有弹性，以便即使对象被故意损坏，也可以提取识别信息。为此，该项目的重点是使用纠错码理论开发在3D打印物体内嵌入信息的鲁棒方法。纠正不利因素引起的错误是编码理论中一个研究得很好的主题，并且存在许多好的代码。然而，由于攻击者可以物理篡改3D对象，因此3D打印信息容易出现现有代码无法解决的独特噪声模式。在拟议的工作中，这些模式将被分析和数学抽象，并将开发新的代码来打击任何有限的对手。该项目旨在开发3D信息嵌入中最坏情况下鲁棒性的实用机制，以及新的编码理论概念和概念验证级别的实验工作。该奖项反映了NSF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。