A Practical Print-and-Scan Resilient Watermarking

A Practical Print-and-Scan Resilient Watermarking for High Resolution Images Yongping Zhang1 , Xiangui Kang2, and Philipp Zhang3 1

Research Department, Hisilicon Technologies Co., Ltd, 100094 Beijing, China. [email protected]. 2 School of Infor. Sci. and Tech., Sun Yat-Sen University, 510275 Guangzhou, China. [email protected] 3 Research Department, Hisilicon Technologies Co., Ltd, 750075 Plano, Texas, USA. [email protected]

Abstract. Detecting the watermark from the rescanned image is still a challenging problem, especially for high resolution image. After printing and scanning, an image usually suffers global geometric distorts ( such as rotation, scaling, translation and cropping), local random nonlinear geometric distortions which is simulated in Random Bending function of Stirmark, as well as nonlinear pixel value distortions. The combination of both of the latter distortions is called nonlinear distortions. Many watermarking techniques, including the pilot-based watermarking techniques, are robust against the global geometric distortions but sensitive to the nonlinear distortions. Local random nonlinear geometric distortions remain a tough problem for image watermarking. In the setting of print-and-scan process, it becomes severer as combining with nonlinear pixel value distortions. This may defeat a watermarking scheme, especially for the print-scanning of high resolution image. This paper proposes an effective pilot-based watermarking algorithm for the print-and-scan process. After careful analysis of the print-and-scan process, the pilot signal and the watermark are embedded and detected in the downsampled low resolution image to deal with the combination of local random nonlinear geometric distortions and nonlinear pixel value distortions. Theoretical analysis and experimental results demonstrate the proposed algorithm is robust to the print-and-scan process and at low computational complexity. The major contribution of this paper is that we analyze the impact of nonlinear distortions in print-and-scan process and propose an effective watermarking scheme to conquer it. To our best knowledge, our work also first addresses the issues of print-and-scan resilient watermarking for high resolution images. Keywords: Print-and-scan, watermark, non-linear distortions, robustness

1

Introduction

The print-and-scan process is commonly used for image reproduction and distribution. Print-and-scan resilient data hiding provides a viable authentication mechanism via

the multi-bit watermark hidden in a picture in the document. Document authentication of ID card, passport, driving license, image publication etc. is important today as the security concerns are higher than ever before. For example, the authentication of South China Tiger Image drew extensively concerns in China, even in the whole world. But print-and-scan resilient data hiding has not been extensively researched. Many of them focus on detecting watermarks [1]. The rescanned image is usually changed both in geometric and pixel value, and in both linear, non-linear way. The watermark can be detected from the rescanned image only if the watermark can resist the combination of these distortions. Many watermarking techniques robust to geometric distortions are presented. One category is to embed the watermark in the global geometric transform invariant domain [1][2]. The computational complexity of this technique is high, especially for high resolution images. Besides, the non-uniform log-polar mapping adopted in this technique might cause severe image fidelity problem. Another category[3][4][5] is to embed a pilot multiple times in the image at different spatial locations. The autocorrelation function of the watermarked image will then yield a pattern of peaks corresponding to the embedded locations. Changes in this pattern of peaks can be used to estimate the global geometric distortions that the watermarked image has undergone. These methods have significant potential, but, similar to the above methods, do not consider the effect of the nonlinear distortions including nonlinear geometric distortions and nonlinear pixel value distortions. The watermark can be detected based on the pilot signals. But after printing-and-scanning, the nonlinear distortions destroy the periodicity of the pilot signal and the autocorrelation of the rescanned image will not yield a pattern of peaks. After careful analysis of the print-and-scan process, our proposal employs a downsampling operation in the watermark embedding and extraction process that simulate the watermarking embedding and extraction in the low-resolution, thus reduce the effect of nonlinear distortions and make sure that the pilot signal can be detected correctly. We can get the parameters of the global geometric distortions with the help of pilot signal and invert the rescanned image before applying the watermark extractor. In this paper, we begin with the analysis of the print-and-scan process in Section 2. Then we propose a novel watermarking algorithm in Section 3. Experimental results are presented in Section 4. In Section 5, we make a summary.

2 Nonlinear Distortion Problems to Watermarking and Solutions In this section, we first make a careful analysis on the print-and-scan process. Here we present how the print-and-scan process affects the watermark. Then we propose the following solutions.

2.1

Analysis on the Print-and-Scan Process

The process of print and scan is a complex composite of various attacks, which cause various distortions including geometric distortion and pixel value distortion. The original image is transmitted into control system of the printer. Before Raster Image Processor (RIP) in the control system converts the original image into halftone image and corresponding electronic pulse level, the printer will make the Dots Per Inch (DPI) value of image equal to the physical DPI value of the printer. The electronic pulse level is converted into optical level and forms latent image on Organic Photo Conductor (OPC) drum of electrophotographic system. After development process, transferring process and fusing process, latent image is transformed into hardcopy output. In the scanning process, a pure light emitted to the hardcopy global. Charge Coupled Device (CCD) sensors receive the luminance of the light and convert it into the electronic signal. At last, the generated digital image generated is transmitted to PC via Graphic User Interface (GUI).[7] According to the process of the print-and-scan process, we can find that both the printing process and scanning process introduce geometric distortions. The distortions during these two processes are different. Converting the electronic pulse into optical level and hardcopy process introduces the nonlinear geometric distortions. Consider an image I, and a nonlinear distorted version of this image, I’. Then we can write that (1)

D ( x, y ) = I ' ( x , y ) − I ( x , y )

where D(x,y) represents the difference of pixel value between I and I’. Let ∆x and ∆y represent the magnitude of nonlinear geometric distortion in the horizontal direction and in the vertical direction respectively. The position of pixel I(x,y) is translated to the position ( x + ∆x , y + ∆y ) in the rescanned image. The nonlinear adjustment in the printer and scanner also introduce the distortion on the value of pixel. The nonlinear adjustment, which is called gamma tweaking, is performed in the printer to make sure the printed images appear the same as on a monitor: γ (2) B = I ( x, y ) p where B represents the actual light brightness. When the image is scanned, it must be compensated to make sure it looks fine to us when viewed on a monitor: 1

I ' ( x, y ) = B

γs

(3)

So, γp

I ' ( x, y ) = I ( x, y )

γs

(4)

Besides these distortions, there is some random noise in the print-and-scan process. And let N represent the random noise. Then we can get γp

Dx , y = I ' ( x, y ) − I ( x, y ) = I ( x − ∆x, y − ∆y )

γs

+ N − I ( x, y )

(5)

When γ p > γ s , this nonlinear adjustment amplify the effect of nonlinear geometric distortion. And D is distributed in the whole image. It may become large enough to affect the watermark extracting. There is one measure to deal with this problem if the resolution of image is low. We can scale the watermarked low resolution image up to a higher resolution before printing it out. And in the scanning process, the printed watermarked image is downsampled to a low resolution as the original resolution. This operation can improve the robustness against the nonlinear distortions. Some experiments on Digimarc watermarking tool in Photoshop are performed. In our experiments, 100 images with 800-by-600(4:3) are utilized. The embedding strength is as large as possible under the limitation of invisibility and the PSNR value is 36.13dB averagely. It shows good performance when we up-scale the watermarked image to 4096-by-3076(4:3, which is the size of A4) in the printing process. The Bit Error Ratio (BER) of watermark extraction from the rescanned watermarked images is about 13.15%. But unfortunately, with the development of the multimedia technologies, the resolutions of images become higher and higher in the application. Especially in the document printing, the driver of printer will convert a text to an image. The resolution of this image is very high and based on the size of paper printed on. Now the above measure does not work. For example, when the watermarked image whose resolution is 4096-by-3076 is printed on A4, we cannot up-scale the watermarked image because of the paper scope. Now the nonlinear distortions can destroy the watermark easily. Experiments on the Digimarc watermarking tool are performed on four images with 4096-by-3076 or 3076-by-4096. The watermarked images are printed with the original resolution on A4 paper. No watermark can be extracted from the rescanned watermarked images. Another problem is that many existing print-and-scan resilient watermarking algorithms are complex and time consuming. Many complex methods are included to make the watermarking algorithm to be robust against the print-and-scan process. Computation efficiency is an important consideration in application. There is still another measure proposed in this paper to deal with the above problems. We can adopt the method of embedding and extracting watermark at low resolution shown as follows to get better robustness against nonlinear distortions and higher computation efficiency. 2.2

Proposed Solution

Based on the above analysis and the experiments with Digimarc watermarking tool on the print-and-scan process, our solution is to add a down-sampling operation to achieve the low-resolution which is necessary in the watermark embedding and extraction process. In watermark embedding, we first down-sample the original image with k and embed the watermark in the down-sampled image. Let I w ( x , y ) represent one pixel in the down-sampled watermarked image. We can obtain,

k

∑ I ((x − 1)× k + i , ( y − 1)× k + j ) w

I w ( x, y) =

i , j =1

(6)

k2

Assume Iw(x,y) to be the pixel of the watermarked image and I w' ( x, y ) to be the pixel of the rescanned watermarked image, where

 x = ( x − 1) × k + i i, j ∈ [1, k ]   y = ( y − 1) × k + j

(7)

In watermark extraction, the rescanned image is also down-sampled to 1/k of the original size. We can obtain, k

' w

(x , y ) =

∑ I ((x −1)× k + i , (y −1)× k + j ) ' w

(8) k2 The difference between Eq.(6) and Eq.(8), which is the effect on the image at low resolution from the nonlinear distortion, can be described as, I

i , j =1

k

'

I w ( x , y ) − I w ( x, y ) =

∑D i, j

k

2

i, j

=

D k2

(9)

k

where D = D and ∑ i, j

Di , j = Iw' (x, y) − Iw (x, y ) = Iw' ((x −1) × k + i , (y −1) × k + j ) − Iw ((x −1) × k + i , (y −1) × k + j )

,

i , j =1

which is shown in Eq.(5). The impact of random noise N in Eq.(5) can be removed or lessened with the summation operation in Eq.(9). Most importantly, the nonlinear geometric distortion in a down-sampled rescanned image will be reduced to 1 / k 2 of the distortion in a rescanned image without down-sampling. If k is great enough, the effect of nonlinear geometric distortion almost may be ignored.

3

Proposed Scheme

The key idea of our proposed scheme is to embed and detect the watermark in low resolution. This will reduce the effect of nonlinear geometric distortion and ensure the survival of watermark from the print-and-scan process. The diagram of our watermarking scheme is illustrated in Fig. 1. 3.1

Watermark Generation

In our watermarking algorithm, there are two types of watermarking signal. One is pilot pattern p, which is a white noise pattern and embedded in certain blocks. Through the extraction of pilot patterns, the rescanned image can be resynchronized. The other is watermark payload {bi } . The watermark payload is often chosen to be orthogonal noise-like patterns or spread spectrum modulated by a basis of n orthogonal noise-like patterns {ui} via,

Fig. 1. Block diagram of the proposal scheme. (a) is watermark embedding process. (b) is watermark extraction process. n

w = ∑ bi ui

(10)

i =1

where

3.2

bi ∈ {0,1} or bi ∈ {−1,1} [8]. Watermark Embedding in Low Resolution

In order to resist the nonlinear geometric distortions introduced in the print-and-scan process, the low-resolution image based watermark embedding method is proposed. The main steps are as follows. Step 1 Down-sample the original image I to 1/k of the original size to get I . In our experiments, we use k=10. Step 2 Divide I into the blocks and perform Discrete Cosine Transform (DCT). Step 3 Embed the pilot pattern p and watermark pattern w in DCT coefficients

f of different blocks (see Fig. 2).

Fig. 2. The partition model. The pilot patterns are embedded in the black blocks. And the watermark patterns are embedded in the other blocks. The gray regions are exhaustive searching windows.

f w ( x , y ) = f ( x , y ) + αw

(11)

where α is the watermarking strength. We use α = 0.6 in our experiments. And here p is a white noise pattern and w is a set of white noise patterns. Here we use the popular linear, additive watermarking scheme[8] in the watermark embedding. Other embedding schemes, such as linear[10][11] and nonlinear multiplication watermark scheme[12], may be used to achieve better performance in terms of robustness or invisibility of watermark. Step 4 Perform inverse DCT transform on the watermarked DCT coefficients fw of every block and get I w . Step 5 Get the watermark mask as, Mask = I w − I

(12)

Step 6 Up-scale Mask with k to the original size and add it to the original image to get the watermarked image Iw. 3.3

Watermark Extraction

The watermark extraction procedure includes three steps. Step 1 Down-sample the rescanned image at different ratio and detect the positions of four pilot patterns by exhaustive searching in the certain windows (see Fig. 2).

Assume Rimg to be the resolution of the original image and Rscan to be resolution of the scanner. It is well known that the difference between Rimg and Rscan will introduce scaling between the original image and the rescanned one. And the scaling parameter is Pscale =

Rscan Rimg

(12)

In order to extract the pilot signal in the low resolution, we also down-sample the watermarked image with 1/k. According to Section 3.2, the effect of nonlinear geometric distortions can be reduced. So the possible ratio of down-sample is

Rate =

Rimg

(13)

k ⋅ Rscan

Rscan is certain in the extraction process. And although Rimg is unknown to the detector, the possible value is limited in application. We will exhaustive searching in all possible value of Eq.13. We can get the searching result at which the correlation between the original pilot signal and the extracted one is highest. Down-sample the rescanned image with that result ratio and find the locations of four pilot signals in the down-sampled rescanned image. Step 2 Get the parameters of global geometric distortions. With the help of four pilot signal, we can get rotation angles and four sets of parameters of translation[7]. We use the average values of them to resynchronize the down-sampled rescanned image. Because the positions of four pilot signals can be gotten correctly, we can get the exact orientation with their help. Step 3 Decode the watermark payload bit by bit from the recovered image. The down-sampled image is divided into blocks. Select the DCT coefficients of blocks at the same positions as in the embedding procedure. Compute the correlation between ui and the selected coefficients. If the correlation is greater than a threshold T, then indicate that the bit of watermark bi is “1”.

4

Experimental Results

We evaluate the proposed algorithm using 4 high resolution images from Standard High Precision Pictures (SHIPP). The parameter of down-sample k is set to 10. The down-sampled image is divided into blocks with size 64-by-64 and there are 24 blocks in one test image. Except four blocks embedded the pilot signals in, we embed 64bits in each of the other twenty blocks. So the largest watermarking capacity is up to 1280 bits. In our application, we embed the same watermark in these twenty blocks and the watermarking capacity is 64 bits. The PSNRs of the watermarked images are presented in Table 1. From this table, we can find that visual quality of the watermarked images is acceptable. And the watermarked images are shown in Fig. 3.

Table 1. PSNRs of the watermarked images. Image Bride Harbor Wool Bottle Resolution 3072-by-4096 4096-by-3072 4096-by-3072 3072-by-4096 PSNR(dB) 36.07 33.49 33.80 33.25

Fig. 3. Watermarked images

We printed the watermarked image out by Ricoh Aficio 2032 and scanned the printed watermarked images by HP scanjet 8250, both common commercial products. We employ different models of Ricoh laser printer and HP scanner to evaluate the robustness of our proposed watermarking algorithm. The resolution of Aficio 2032 is set to 600 dpi. And the scanning resolution is set to 300 dpi and 600 dpi. The detection results are shown in Table 2. The BER of extrcted 64 bits is less than 1.6%. So we can conclude that the proposed algorithm is robust to print-and-scan process. Table 2. The detection results BER(%) Image Scanning Resolution Scanning Resolution = 300 dpi = 600 dpi Bride 0 0 Harbor 0 0 Wool 0 0 Bottle 1.6 0

We also evaluate the proposed algorithm’s robustness against JPEG compression. The watermarked images are compressed under different JPEG quality factors. The quality factors are set to be from 50 to 90 and at intervals of 10. All watermarks can be extracted correctly without any error bit from compressed images. In the experiments, the watermarking algorithm is implemented with CPU PIV 2.4G, RAM 2G and Matlab 6.5. It takes about 7.89s for the embedding, and takes 216.08s for the extraction despite the very high resolution of images, so it is efficient in computation.

5

Conclusions

In this paper, we analyze the impact of the combination of local random nonlinear geometric distortions and nonlinear pixel value distortions in print-and-scanning process, and a novel print-and-scan resilient image watermarking algorithm is proposed. The watermarks are embedded in the down-sampled low resolution image to reduce the effect of nonlinear distortions in printing process. And the pilot patterns are embedded to be resilient global geometric distortions in print-and-scan process. Theoretical analysis and the experimental results demonstrate that the proposed algorithm is robust to the print-and-scan process and its computational efficiency is high. Future work is to perform more testing on this algorithm by more test images.

Acknowledgement This work was supported by NSFC (60403045), NSF of Guangdong (04009742), and NSF of Guangzhou (2006Z3-D3041).

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