ITR: Printer Characterization and Signature-Embedding for Security and Forensic Applications

提案编号CCR-0219893标题：ITR：用于安全和法医应用的打印机表征和签名嵌入共同主要研究者：Jan P. Allebach、Edward J. Delp和乔治T.我们提出了两种打印机识别策略。第一种策略是被动的。它涉及表征打印机并在打印输出中找到该特定打印机、型号或制造商产品的固有特征。我们称之为内在特征。开发固有签名需要对打印机机制的理解和建模，以及开发图像分析工具，所述图像分析工具用于在签名开发阶段期间的打印机表征，然后用于在具有任意内容的打印页面中的签名的实际检测。通过用高分辨率鼓式扫描仪扫描打印页面来检测固有签名，以及应用低级图像分析例程来提取特征。这些特征用软分类器进行处理，以在决策树的每个级别产生文档是用属于每个特定类别的设备打印的可能性。在决策树的最高级别，提供了使用两种可能的主导打印技术中的哪一种的可能性：电子照相术（通常称为激光打印机）和喷墨。在下一个层次，生成候选打印机制造商的可能性，等等。当我们沿着树向下进行时，我们生成关于越来越多的特定于所讨论的特定打印机的信息的可能性。第二个策略是主动的。在这里，我们在每个打印页面中嵌入一个外部签名。该签名是通过调制打印机机构中的过程参数来生成的，以在每个打印页面中编码识别信息，例如打印机序列号和打印日期。为了检测外部签名，我们再次扫描打印页面，并使用图像分析技术对其进行处理;但在这种情况下，我们的目标是解码签名以提取嵌入其中的信息。外部签名嵌入方法的开发将直接建立在我们对内部签名的工作基础上。我们将使用我们的知识的打印机机制模型和打印机表征的结果，以确定打印机的过程参数，可以调制编码所需的识别信息。这些参数的调制将需要修改实际的打印机mechanism.A提出的努力的显着特点将是一个本科项目课程，将与研究的发展。在本课程中，学生将学习印刷技术和电气和机械工程理论的应用，从他们的核心课程到印刷系统的分析和建模。他们还将学习图像处理和决策理论;他们将看到所有这些工具如何应用于解决实际的现实问题。

Proposal Number CCR-0219893Title: ITR: Printer Characterization and Signature-Embedding for Security and Forensic ApplicationsCo-Principal Investigators: Jan P. Allebach, Edward J. Delp, and George T. ChiuWe propose to develop two strategies for printer identification. The first strategy is passive. It involves characterizing the printer and finding intrinsic features in the printed output that are characteristic of that particular printer, model, or manufacturer's products. We call this the intrinsic signature. Developing the intrinsic signature requires an understanding and modeling of the printer mechanism, and the development of image analysis tools that are used for printer characterization during the signature development phase, and then later, for the actual detection of the signature in printed pages with arbitrary content.The intrinsic signature is detected by scanning the printed pages with a high resolution drum scanner, and applying low-level image analysis routines to extract features. These features are processed with a soft classifier to yield likelihoods at each level of a decision tree that the document was printed with a device belonging to each particular class. At the highest level of the decision tree, likelihoods are provided for which of the two possible dominant printing technologies: electrophotography (commonly referred to as a laser printer) and inkjet was used. At the next level,likelihoods are generated for the candidate printer manufacturers, and so on. As we proceed down through the tree, we generate liklihoods regarding information that is more and more specific to the particular printer in question.The second strategy is active. Here we embed an extrinsic signature in every printed page. This signature is generated by modulating the process parameters in the printer mechanism to encode identifying information, such as the printer serial number and date of printing, in every printed page. To detect the extrinsic signature, we again scan the printed pages, and process them using image analysis techniques; but in this case, our goal is to decode the signature to extract the information embedded in it. Development of the methodology for extrinsic signature embedding will build directly on our work with intrinsic signatures. We will use our knowledge of the printer mechanism models and the results of the printer characterization to determine the printer process parameters that can be modulated to encode the desired identifying information. The modulation of these parameters will require modification to the actual printer mechanism.A distinguishing feature of the proposed effort will be the development of an undergraduate project course that will be associated with the research. In this course, students will learn about printing technologies and the application of electrical and mechanical engineering theory from their core courses to analysis and modeling of printing systems. They will also learn about image processing and decision theory; and they will see how all these tools can be applied to the solution of practical real-world problems.