Optimized Embedding and Decoding of Extrinsic Signatures for Electrophotographic Halftone Images

Printer identification based on a printed document can provide forensic information to protect copyright and verify authenticity. In this work, a stochastic dot interaction model that take in to consideration of scanner characteristics and print-scan channel noise is developed to predict the impact of embedding extrinsic signatures using laser intensity modulation. With this model, reflectance of the printout can be effectively estimated without extensive measurements. In addition, we proposed an optimization framework to select the modulation parameters that will maximize the embedding capacity and detection reliability. Preliminary analysis results such as achievable capacity and correct detection rate are discussed. Introduction To identify the printing device or authentication of the document, features such like banding or streaking generated by modulating EP process can be designed and utilized to help the extrinsic signatures. In previous work [1], we exploited the properties of pseudo-random noise (PN) sequences in developing associated embedding and detection algorithms. The unique crosscorrelation properties of the PN sequence enabled decoding without the need of sequence synchronization [2], which improve the resistance of the proposed method to cropping attack. The embedding capacity can be increased by segmenting the image into rows with a different code embedded to each row. Significant amount of printing and measurements are needed to identify the proper modulation parameters that are both time consuming and wasteful. A laser intensity modulation EP model [3] was developed to facilitate the development of optimal modulation parameters and reduce the required time and effort. Given a set of intensity modulation parameters, the model can effectively estimate the reflectance of the output image. With developed modulation model, Not only the image quality can be estimated but also the embedding and detection algorithm can be developed efficiently to increase the capacity and detection performance. In this work, we will exercise the modulation model developed in [3] by including appropriate image process algorithms and a suitable image quality metric. An optimization problem is then formulated and solved to identify a set of modulation parameters that can maximize the embedding capacity while maintain image quality. The paper is organized as follows. Laser intensity modulation model will be discussed in the next section followed by the description of the image quality metric used in the study. Embedding algorithm and capacity analysis will be presented in the fourth section followed by the discussion of the detection algorithm. The optimization problem is described in the fifth section. Conclusions and future work are given in the last section. Laser Intensity Modulation Model In previous work [3], a laser intensity modulation model using stochastic dot interaction has been shown to be effective in predicting the reflectance of the halftone image for low embedding frequencies. The inclusion of the scanner modulation transfer function (MTF) can further improved the high frequency accuracy of the overall model. Figure 1 shows the proposed structure of the laser intensity modulation model for estimating the resulting image quality when embedding extrinsic signatures. In addition to the inclusion of the scanner MTF, a noise model is also developed to include the structured noise in the print-scan channel. In this section we will discuss the inclusion of the scanner MTF and the channel noise model in the overall system model. Figure 1. Block diagram of the laser intensity modulation model used in estimating the impact of signature embedding on image quality. Sacnner Modulation Transfer Function (MTF) In this work, an Epson Perfection 3200 Photo with 600 dpi scanning resolution is used. To characterize the scanner MTF, a SINE M-13-60-1X pattern array is used as the test target. The detail instructions and techniques for characterize the MTF of a scanner can be found in [4]. Following [5], the scanner MTF is modeled as the weighted sum of two Gaussian functions, i.e.