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

3D打印带来了一场消费和商品分配的革命，但也给公共安全带来了重大风险。现在，任何有互联网接入和商品打印机的人都可以生产无法追踪的枪支（鬼枪）、钥匙和危险的假冒产品。为了对抗这些威胁，在物品中嵌入识别信息是很重要的，这样，如果物品被没收或留在犯罪现场，就可以识别肇事者。在3D打印对象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，信息嵌入技术必须能够抵御敌对篡改，以便即使对象被故意破坏也可以提取识别信息。为此，该项目专注于开发使用纠错码理论在3D打印对象中嵌入信息的稳健方法。在编码理论中，对抗性诱导错误的纠正是一个研究得很好的课题，并且存在许多好的编码。然而，由于攻击者可以物理篡改3D对象，3D打印信息容易产生现有代码无法解决的独特噪声模式。在建议的工作中，将对这些模式进行数学分析和抽象，并开发新的代码来对抗任何有限的对手。该项目预计将开发3D信息嵌入中最坏情况鲁棒性的实用机制，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。

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.