On the Function of Graphic Language in Poster Design

doi:10.21311/001.39.9.30 On the Function of Graphic Language in Poster Design Hong Zhao Anhui Institute of Information Engineering, Wuhu Anhui, 241000, China Abstract Graphic language in this paper refers specifically to the language separated by raster processor through digital poster editing samples confirmed by editors during the digital poster design, and then stored the in binary form. As soon as the poster design is confirmed, the legitimacy of its source is without doubt. Seeing from the entire workflow, there is still a large risk of encryption in the storage and transmission process of the poster design. Judging from the existing replication encryption testing algorithm, there are some problems such as high feature dimension, high computational cost or low detection rate. And this method is not suitable for binary proofs after color separation. This paper presents a method of detecting the Replication encryption based on graphic language features, which is based on the binary quantization of CMYK target image after color separation, the target graphics is segmented by the method of sliding block, and the local graphic features of the four channels of CMYK block graph is extracted so as to do the encryption detection. Experiment results show that this method has a lower time complexity and high detection rate compared with the previous methods on the graphic encryption detection, and it has good robustness to the rotation attack of the encrypted area of the graph, and the small scale zoom attack and so on. Key words: Copy, Encryption Detection, Graphic Language, Digital Graphics Forensics. 1. INTRODUCTION Although the rapid spread of mobile devices such as mobile phones and tablets has led to the partial separation of media from digital media to digital media, many factors, such as appreciation of art, aging of the society and cost of the Internet, The paper media in the future for a long period of time will bear the main force of information dissemination. Therefore, to ensure the authenticity of the information disseminated by the paper media has important social value and significance. Particularly the design of images posters on paper media, due to their rich content, how to ensure the authenticity and credibility of placard design picture source appears to be particularly important. However, in recent years, digital image processing tools (such as: Photoshop) developed continuously, making the picture encryption technology costs and thresholds continue to decrease. According to statistics, about 10% of the United States newly published color photos are modified and done with Post-processing operation of the photo (Birajdara and Mankar, 2013).And in the domestic poster design; the situation of forged pictures of is endless, such as "South China tiger" "square pigeons" and other photo forgery, having a significant negative impact on the society. In view of this, Graphical cryptography detection not only has important application value in news publishing and cultural communication, but also has been widely concerned by academic circles. There are two types of detection methods: active and passive detection(blind detection) (Popescu and Farid, 2004; Huang and Yan, 2016), active detection technology is mainly based on digital watermarking and digital signature, and need to verify the information of the source image, this will change the original information of the source graphics, and in many cases it is impossible to get the original graphics, without any additional information in the case of the authenticity of the target graphics to identify, so the active detection method does not have good practical applications The passive detection technology is to carry on the analysis of the information carried by the graphics to achieve a graphics encryption detection technology, its main advantage is that no need to embed any data to be detected graphics, so do not destroy the original graphics The situation can be achieved on the authenticity of the target graphics detection. In view of the graphics copy encryption detection, many scholars have made fruitful research work. Most of the existing research results are mostly based on block-based detection method and the keypoint based detection method on the image copy and paste encryption and detection. Block-based graphics encryption detection method is to block the graphics by overlapping blocks, and then extract the feature vector from a large number of blocks to characterize the block, and finally sort through the dictionary or the most recent method, (DCT) is proposed in (Ding and Yang, 2007), which is based on discrete cosine transform (DCT). This method uses the DCT to encrypt the image, and then the image is divided into blocks. (DCT), discrete cosine transform (DCT), A method for detecting the image copy-and-paste encryption is proposed, which combines the 225

logarithmic polar coordinates and the wavelet transform to detect and locate the encrypted region of the graphic copy. This method reduces the dimension of the block after the wavelet transform and maps it to the logarithmic polar coordinate system, uses the phase correlation method as the similarity standard between the graphic blocks, and searches the similar blocks in an exhaustive search way. (Ardizzone, Bruno and Mazzola, 2010)The paper studied a method of using a standard texture descriptor to detect a pattern copy region, which uses five standard texture descriptors extracted from a graphical block to characterize the graphic properties. The experimental results show that the statistical texture descriptor is superior to other Texture descriptor has better detection accuracy and faster speed. Here (Liu, Wang, Lian and Wang, 2011) proposed a Hu-invariant moment of the copy encryption detection method, the method from the circular block extraction Hu invariant moment features, experiments results show that this method is robust to such attacks as rotation, blur, JPEG compression, etc. In (Ryu, Lee and Lee, 2010), an encryption detection algorithm for detecting image copy-rotation-movement is proposed, which uses Zernike moments as graph block features, Noise, blur, rotation, etc. In order to enhance the robustness of the algorithm to the large-scale scaling and rotation attack in the image encryption area, (Wu, Wang and Zhang, 2010) proposed a duplicate encryption detection method based on log polar coordinate Fourier transform (LPFT) (LPFFT) detection method based on log polar coordinates (Wu, Wang and Zhang, 2011; Davarzani, Yaghmaie, Mozaffari and Meysam, 2013) proposed a multi-resolution local binary pattern (Multiresolution Local Binary Patterns) graphics copy and paste encryption detection method, This method uses the local binary pattern with different resolution to extract the feature of the block, and improves the detection efficiency by combining the dictionary sorting and the k-d tree method. The method has high detection rate, and has the advantages of rotation, scaling, JPEG compression, which is robust to the rotation and scaling attack of the encrypted area. Furthermore, Li et al. (Cozzolino, Poggi and Verdoliva, 2014) proposed a fast detection method based on the Patch Match algorithm, In (Li, Li, Yang and Sun, 2015), a new method of image segmentation based on image segmentation is proposed, which divides the image into independent semantic blocks, and combines the method based on key points extraction to avoid a large number of non-similar blocks Contrast, in order to improve the detection efficiency, the realization of the graphics copy and paste encryption detection. At present, the work of graphics encryption detection is often used to solve the digital media transmission between the networks whether the encryption occurs, how to encrypt the graphics language detection need to explore a new technical line. In summary, in the entire poster design in this paper, we propose a new encryption algorithm based on graphic language technology, which is based on the technology of graph language, which can be used to detect the encryption of graphics languages, which involves the interdisciplinary knowledge cross-discipline and few related works. In this paper, the graphics language algorithm simulates the binary quantization processing of the CMYK target graphics in the process of graphics processing by the raster graphics processor, and then blocks the target graphics by sliding block method. By extracting four blocks of CMYK, then it is used to detect the encryption of the graphic block. Experimental results show that compared with the traditional digital format image encryption detection method, this method not only has close to even higher detection rate in the encryption detection of graphics language, Which has a low time complexity and can resist attacks such as rotation of the encrypted area, small scale scaling and JPEG compression. The rest of this paper is organized as follows: Section 1 introduces the relevant research work; Section 2 describes the graphic language based on the graphics language copy encryption detection methods; Section 3 for the experimental results analysis and conclusions. 2. GRAPHICS LANGUAGE ENCRYPTION DETECTION BASED ON GRAPHIC LANGUAGE TECHNOLOGY In the graphic poster design process, in order to simulate the continuous visual effects of graphics, generally through the graphics language dot size or frequency changes to simulate the changes in graphics and shadows.graphic graphics in graphics and graphics similar to the source, very good to retain the paper which uses the image invariant feature of image graphics language to deal with the problem. In the process of graphic prepress processing, the dot density feature of graphics is extracted to realize the image copy encryption detection. The basic idea is as follows: Firstly, CMYK color separation processing, and then through the raster graphics processor CMYK graphics language binary quantization processing to get CMYK four-channel graphics language graphics, and then calculate the CMYK each channel plane in the local point density of each pixel, and then the Figure 1 is the basic flow of the graphic language encryption and detection based on the graphic language technology. Firstly, the paper introduces the basic principle of the graphic encryption algorithm based on graphic language. 226

source image CMYK color separation Image block dot density feature Image block similarity detection Raster image processor (halftone The image overlapping and block detection result Figure 1. Graphic language copy and encryption detection workflow 2.1. Algorithm Flow A Graphics Language Encryption Detection Algorithm Based on Graphics Language Technology S1: Source graphics CMYK color separation S2:;Graphical language binary quantization of graphics CMYK four channels using raster graphics processor S3 : Calculate the local density of pixels in the plane of each channel of 1 1 1 CMYK, i j pij f X, Y ; 9 xi y j1 S4: The size of the S3 to get the M N graphics overlap block, block size B b b ; N M b 1 N b 1 these graph block, and then the dot density S5:From S4, we can get B moments of the block VB are extracted according to the formulas (5), (6), (7) and (8); S6: K d Tree Is constructed by the block dot density feature V B which is extracted from N B block The similarity degree of the blocks is matched by the approximate nearest neighbor search method. The similarity calculation method between the eigenvectors is shown in formula (9); d A, B a S7: The degree of similarity between the two graphic blocks A and B is denoted as graph block of d A, B T (for a threshold) labeled as a copy area. xi, j + u b i, j i, j Thresholding t u Kernel k i, j m, n e i, j + - Figure 2. Error diffusion algorithm process procedures 2.2. Graphic digital graphics language binary quantization method In the process of graphic prepress processing, the main function of raster graphics processor is to convert the graphics into a raster binary graphics device that can directly control the output device according to the characteristics of the output device, that is, the graphics language is screened The lattice graphics processor transforms the continuous graph into binary image, which makes the quantized binary image similar to the original image in visual effect, and the generated graphic language graph retains the content characteristic of 227

graphics well. General digital graphics language is divided into three categories: order jitter algorithm, error diffusion algorithm and iterative method. Among them the error diffusion algorithm is widely used in practical applications because of its good trade-off between image quality and computational complexity. In this paper, the error diffusion algorithm simulates the raster graphics processor to perform binary quantization on the graph. Fig 2 shows the processing flow of the error diffusion algorithm. When the continuous graph is processed line by line through graph language, the previous processed error is transferred to the currently processed pixel. For the currently processed pixel x i, j, x' i, j is the error sum of transited adjacent area processed pixel, b indicates the binary quantization result of the pixel, i, j diffusion error. i j, and u i, j is the pixel gray value after adding the i, j ',,,,, i j i j i j i j i, j u x x e u b (1) 1 1 ',, i j im jn m, n m0 n1 x e k (2) b i, j 0 ifu 1 ifu i, j i, j t t (3) (3) is the threshold value, and is usually set to 0. Figure 2, the error diffusion filter; different error diffusion filter can generate different quality graphics language graphics. As shown in Figure 3, (b) for the Lena continuous tone Graphic (a) Graphical language graph after binary quantization through graphical language. (a)(b) Figure 3. Lena picture which is based error diffusion After the binary quantization, the binary image has the following characteristics: (1) the original content of the graphics, such as edge information, texture features, etc.; (2) after the two-valued quantization, after the raster graphics processor generated graphics language binary image has the following characteristics: (3) The graph language of the generated graphs can easily extract the point density feature of the graph, which belongs to the statistical feature quantity, and has a definite effect on the post-processing operation of the encrypted area. (4) The binary quantization of graphics reduces the complexity of subsequent computation. 3. EXPERIMENTAL RESULTS ANALYSIS In order to verify the detection efficiency, robustness and performance of the proposed method, we use Benchmark Data set by Christlein et al (Christlein, Riess, Jordan, Riess and Angelopoulou, 2012) to carry out the comparative experiment. On the basis of Visual C ++, the graphics workstations used in the experiment are: AMDAthlonX43.7GHzCPU, DDR31600HZ8G memory. The following are the evaluation methods and related experimental results used in this experiment. 3.1. Evaluation Methods In this paper, we use the precision (Precision), recall rate (Recall) and comprehensive evaluation index F1- Measure to evaluate the algorithm, precision rate and recall rate is calculated as follows: TP TP precision, Re call (4) TP FP TP FN 228

2 Pr ecision Re call F1 measure Pr ecision Re call Where TP is the number of encrypted pictures correctly detected by the algorithm; FP is the number of the encrypted pictures that are mistakenly detected by the algorithm; FN is the number of the encrypted pictures which is not detected by the algorithm.f1-measure combines Precision and Recall, The average of the indicators. 3.2. Experimental Results Based on preprocessing raster graphics processing flow, the data set Benchmark Data [M] * has been copied to the conventional encryption detection test, rotation attack experiment, zoom (geometry deformation) attack experiment, Gaussian noise and JPEG compression (DCT (Ding and Yang, 2007), PCA (Popescu and Farid, 2004), Zernike Moments (Ryu, Lee and Lee, 2010), SIFT (Christlein, Riess, Jordan, Riess and Angelopoulou, 2012), MLBP (Davarzani, Yaghmaie, Mozaffari and Meysam, 2013)), and the results show that the proposed method is robust and robust. Of the graphics copy encryption detection method in the accuracy, robustness and performance are better performance. The experimental details are as follows: 3.2.1. Routine Testing The Benchmark Data Benchmark data set used in this experiment contains 48 original pictures without any encryption operation and 48 encrypted pictures which have been copied without any other processing except for the translation copy operation and the edge blur processing. The encryption region is difficult to distinguish by the naked eye. We use the proposed method and the above-mentioned four typical methods on the benchmark data set of 96 images were compared test results shown in Table 1. Table 1.Comparison of experimental results of four typical encryption methods Method Precision Recall F1 Method of this paper DCT PCA ZERNIKE MLBP SIFT 84.61 69.22 76.78 81.81 88.23 88.36 91.66 88.00 93.74 79.64 89.57 82.68 93.74 87.37 93.74 90.90 79.16 83.51 Based on the analysis of the experimental results in Table 1 and Table 2, the graphic dot density moments proposed in this paper can describe the local features of graphic blocks well. Based on the graphic dot density moments, The MLBP-based method has better performance than other methods, but the method has a great difference in time cost compared with other methods and methods in this paper. Figure 4: We can see that the method proposed in this paper has a higher recognition degree to the encrypted region. (5) (a)(b)(c) Figure 4. The graphic copy and encryption detection examples based on the picture language point density features 3.2.2. Rotation Attack Experiment and Scale Attack Experiment In order to verify the robustness of the method to the rotation and scaling attack, we use the data of Benchmark Data to expand the scale of the image, The rotation of the 48 images in the Benchmark Data set is rotated at 2, 10, 60, and 180, respectively.figure 5 shows the results of the test in this paper, Examples of rotation attack test results. 229

(a) 2 (b)10 (c)60 (d)180 Figure 5. Rotation attack test examples As shown in Fig. 6, when the image encryption area is rotated, the small area of the pixel is affected by the rotation, but the area of the pixel is much smaller than the pixel area of the overlapped part. The experimental results show that the small angle rotation attack within 10 in the encryption region has no significant effect on the detection effect. For the large angle rotation of 60, the detection of the encrypted region, especially in the case of 180 large-angle rotation, the detection effect is basically the same as the case where the encryption region is not rotated, and the experimental results are in agreement with the theory. Therefore, the proposed method based on graphics language graphics, the dot - density - moment - based graphic encryption detection method is robust to rotation attack. Figure 6. The impact of rotation attack on features extraction From the experimental results, it can be seen that the small-scale scaling attacks in the scale range of 91% - 109% do not influence the scaled attack area of 80% -120% in the zoom attack expansion dataset. The result of the experiment shows that the proposed algorithm can deal with the problem that the scale factor of the image encryption area is between 91% and 100%, and the scale factor of 80% and 120% 109% of the small-scale scaling attacks, with the encryption area scaling increased detection rate decreased due to large-scale graphics 230

zoom will cause significant distortion of the zoom area, using the naked eye will be easier to detect, so in practical applications, The graphics encryption area is generally not too large scale scaling. 3.3. Algorithm Performance Comparison At present, the performance of the common graphics copy encryption detection method is mainly affected by three aspects: graphics preprocessing, graphic feature extraction, and graphic feature matching. This paper compares the methods proposed in this paper with the four typical methods in computing performance The time spent on graphics preprocessing is denoted as P-Time, the time spent on pattern feature vector extraction is denoted by F-Time, and the time spent on pattern matching is recorded as the time cost of pattern matching. Is M-Time, and the total processing time is denoted as O-Timetable 2 is the average processing time overhead of the proposed method and the other four typical methods. Table 2. Comparison between method of this paper and five types of typical picture encryption detection method experimental result (Unit: second) Method P-Time F-Time M-Time O-Time Method of this paper 0.0965 1.5900 13.5907 15.4585 DCT PCA ZERNIKE MLBP 0.0512 43.4550 24.7330 68.3798 0.0507 68.8947 13.7268 82.7982 0.0516 1.9846 15.8684 18.0192 0.0510 35.1734197.6072232.8316 SIFT 0.1435 0.1903 0.1993 0.9787 Graph feature extraction and feature matching are the main factors that affect the computation time cost of block-based graph copy-paste encryption detection method, and the extracted feature vector dimension of graphics block directly affects the subsequent feature matching efficiency. In this paper, In the block detection method, the DCT-based method extracts the transform coefficients to form the 256-dimensional eigenvector by discrete cosine transform of the block, while the feature extraction in the frequency domain and the similarity matching of the high-dimensional features require high time The PCA method reduces the time cost required for feature matching, but increases the feature extraction time of the block. The PCA method based on the Zernike moment extraction the feature vector is 12-dimension, and the computation time is obviously saved compared with the former two methods. MLBP-based methods need to use many LBP descriptors for feature extraction, such as using the extracted features to form the features of 59, 135, and 18 respectively Vector matrix, and then based on dictionary ordering and k-d tree similar to the subsequent block matching processing, the need to deal with a number of high-dimensional feature vector matrix, an increase of the time complexity of the algorithm. This paper uses CMYK channel extracted from the local graphic features 8-dimensional eigenvector, which has lower feature dimension compared with the above methods, which can save the time cost of similar block matching. Experimental results show that, under the premise of comprehensive consideration of detection rate and detection performance, (DCT, PCA, ZERNIKE, MLBP) is superior to other block-based detection methods in the feature extraction, pattern feature matching and overall detection performance, and the detection method based on key point (SIFT) The detection method only looks for the high entropy point of the graph, and is superior to the block-based detection method in performance, but the sparseness of the key points leads to a lower key point-based method than the block-based method Recall rate. 4. CONCLUSION At present, there is little literature on the research of copy-and-paste encryption detection of graphics languages. Therefore, this paper presents a method to extract the graphic dot density of graphic language to detect copy and encryption the graphic language. First, do the CMYK color separation of the target graphics, then graphics are processed by prepress raster graphics processing, and the k-d tree is constructed by extracting the local graphic features of each channel of CMYK to realize the encryption detection of the graphs. The algorithm is simple and the features extracted by this method and have low dimensionality and rotation invariance. Experimental results show that the method has good detection effect on the image copy and paste encryption detection and has good robustness to the replication region rotation attack, And also has good robustness to small-scale scaling attacks and JPEG compression attacks in the replication area, and it is obviously superior to other block-based graphics copy encryption detection methods in time complexity. ACKNOWLEDGMENTS This work was supported by Research project of Anhui Institute of Information Engineering; Provincial key projects of Anhui Province (SK2016A0124, SK2016A0126);National Undergraduate Innovation Training 231

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