Printer Identification Based on Texture Features

In today’s digital world securing different forms of content is very important in terms of protecting copyright and verifying authenticity. Many techniques have been developed to protect audio, video, digital documents, images, and programs (executable code). One example is watermarking of digital audio and images. We believe that a similar type of protection for printed documents is very important. In this paper we describe the use of image texture analysis to identify the printer used to print a document. In particular we will describe a set of features that can be used to provide forensic information about a document. We will demonstrate our methods using 10 EP printers. This research was supported by a grant from the National Science Foundation, under Award Number 0219893. Address all correspondence to E. J. Delp at ace@ecn.purdue. edu Introduction In today’s digital world securing different forms of content is very important in terms of protecting copyright and verifying authenticity. [1, 2, 3] One example is watermarking of digital audio and images. We believe that a marking scheme analogous to digital watermarking but for documents is very important. [4] Printed material is a direct accessory to many criminal and terrorist acts. Examples include forgery or alteration of documents used for purposes of identity, security, or recording transactions. In addition, printed material may be used in the course of conducting illicit or terrorist activities. Examples include instruction manuals, team rosters, meeting notes, and correspondence. In both cases, the ability to identify the device or type of device used to print the material in question would provide a valuable aid for law enforcement and intelligence agencies. We also believe that average users need to be able to print secure documents, for example boarding passes and bank transactions. There currently exist techniques to secure documents such as bank notes using paper watermarks, security fibers, holograms, or special inks. [5, 6] The problem is that the use of these security techniques can be cost prohibitive. Most of these techniques either require special equipment to embed the security features, or are simply too expensive for an average consumer. Additionally, there are a number of applications in which it is desirable to be able to identify the technology, manufacturer, model, or even specific unit that was used to print a given document. We propose to develop two strategies for printer identification based on examining a printed document. The first strategy is passive. It involves characterizing the printer by finding intrinsic features in the printed document that are characteristic of that particular printer, model, or manufacturer’s products. We shall refer to this as the intrinsic signature. The intrinsic signature requires an understanding and modelling of the printer mechanism, and the development of analysis tools for the detection of the signature in a printed page with arbitrary content. The second strategy is active. We embed an extrinsic signature in a 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. To detect the extrinsic signature we use the tools developed for intrinsic signature detection. We have successfully been able to embed information into a document with electrophotographic (EP) printers by modulating an intrinsic feature known as “banding”. This work in is discussed in [7]. We have previously reported techniques that use the print quality defect known as banding in electrophotographic (EP) printers as an intrinsic signature to identify the model and manufacturer of the printer. [8, 9, 10] We showed that different printers have different sets of banding frequencies which are dependent upon brand and model. This feature is relatively easy to estimate from documents with large midtone regions. However, it is difficult to estimate the banding frequencies from text. The reason for this is that the banding feature is present in only the process direction and in printed areas. The text acts as a high energy noise source upon which the low energy banding signal is added. One solution is to find a feature or set of features which