SaTC: TTP: Small: Creating Content Verification Tools to Protect Document Integrity

SaTC：TTP：小型：创建内容验证工具来保护文档完整性

• 批准号: 2024300
• 负责人: Yao Liu
• 金额: $ 50万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024300&HistoricalAwards=false
• 关键词: SaTC; TTP; Small; Creating; Content

Adobe’s Portable Document Format (PDF) has become the standard for electronic documents. Academic and collegiate papers, business write-ups and fact sheets, advertisements for print, and anything else meant to be viewed as a final product make use of the PDF standard. End users cannot easily change the text of a PDF document, so most come to expect a degree of integrity present in all PDF documents encountered. Nevertheless, content masking attacks were discovered against the content integrity of PDF documents themselves. Specifically, these attacks cause humans to view a masked version of the content these computer systems read. This project will create techniques and tools to deal with content masking attacks against document integrity, and has the potential to significantly impact on the research and engineering effort in the field of document security, due to the wide use of digital documents in government and commercial entities. With the substantial security enhancement for PDF content integrity, the project could promote the wide adoption of PDF content verification tools in today's data loss prevention systems.This project will improve the state-of-the-art in document security via creating a set of defense tools to assure the integrity of documents. The research team aims at (i) designing a lightweight PDF font verification tool that can effectively verify the integrity of font files embedded into documents; (ii) understanding the impact of adversarial machine learning on document integrity; (iii) creating an advanced content verification tool, which integrates functionalities of random partition, feature extraction, and decision aggregation, to address content masking attacks leveraging the adversarial machine learning techniques; and (iv) performing comprehensive evaluation to ensure the efficiency, reliability, and security of the designed content integrity verification tools.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.

Adobe的可移植文档格式（PDF）已成为电子文档的标准。学术和大学论文、商业文章和情况说明书、印刷广告以及其他任何被视为最终产品的东西都使用PDF标准。最终用户无法轻松更改PDF文档的文本，因此大多数人都希望遇到的所有PDF文档都具有一定程度的完整性。然而，内容掩蔽攻击被发现对PDF文档本身的内容完整性。具体来说，这些攻击导致人类查看这些计算机系统读取的内容的屏蔽版本。该项目将创建技术和工具来处理针对文档完整性的内容掩蔽攻击，并有可能对文档安全领域的研究和工程工作产生重大影响，因为数字文档在政府和商业实体中的广泛使用。随着PDF内容完整性的安全性大幅提升，该项目可以促进PDF内容验证工具在当今的数据丢失预防系统中的广泛采用。该项目将通过创建一套防御工具来提高文档安全性的最新水平，以确保文档的完整性。该研究团队的目标是（i）设计一个轻量级的PDF字体验证工具，可以有效地验证嵌入文档的字体文件的完整性;（ii）了解对抗机器学习对文档完整性的影响;（iii）建立一个先进的内容核实工具，该工具集成了随机划分、特征提取和决策汇总等功能，利用对抗性机器学习技术解决内容掩蔽攻击;（iv）进行综合评估以确保效率、可靠性该奖项反映了NSF的法定使命，并被认为是值得支持的，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。