基于混沌神经网络的智能型数字水印算法
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摘要
本文旨在进行基于混沌神经网络的智能型数字水印算法的研究。获得鲁棒性高、不可见性好、数据容量大的含水印图像是本文的目的,混沌神经网络是工具,应用混沌神经网络是为了寻找最佳的水印嵌入强度。
数字水印按照隐藏的位置不同可以划分为空(时)域数字水印和变换域数字水印。在变换域数字水印技术中,由于小波变换的多分辨率特性与人眼视觉系统特性相似,因此本文选择在宿主图像小波变换的基础上嵌入水印图像。为了使含水印图像在鲁棒性和不可见性二者之间达到一个很好的折衷,本文设计将混沌神经网络的能量函数设置为含水印图像的区域内空间频率的负值,借助网络的搜索寻优特性,得到使含水印图像视觉效果最好(空间频率最大)的水印嵌入强度。水印嵌入强度由混沌神经网络确定,而非人们据经验或实验分析得到,体现了算法的智能性。实验过程中还要针对图像特点选择适合的嵌入区域,才能得到令人满意的含水印图像。最后针对实验所得到的结果图像进行常见的攻击,再提取水印进行分析,证明本文算法的可行性。
In the twenty-first century, Internet becomes unprecedented grand development. We can see the footprint of it at every corner in our daily life. It has advantage and disadvantage. The Internet was commendable but also digital media can easily be stolen, modified, copied and disseminated, with the attendant problem caused by information security, copyright issues and copyright disputes has become an increasingly serious social problem. Only depending on password protection can not completely effectively solve the problem, then digital watermarking technology comes up. Digital watermarking technology is an important branch of information hide technology, It is mainly through the media tag information embedded in the signal (digital watermark), Provide an effective technical means for media information in the field of anti-counterfeiting, tamper-resistant, certification, protection of data security and integrity.
Recently, digital watermarking technology has been a certain amount of development. It has a wide range of algorithm and implementation, but different algorithms have their own deficiencies, how to find an algorithm making the image embedded watermark in all aspects to achieve satisfactory results? How to get the target rapidly, accurately and clearly? Of course, we can continue to optimize the algorithm, or use some existing tools to achieve it. Chaotic neural network is a good instrument. Chaotic neural network not only has the characteristic of chaos such as randomness, regularity, ergodicity of the initial sensitivity and the characteristics of chaotic attractor, but also has the features of neural network such as with massively parallel processing, distributed information storage, self-adaptive self-organization and association study, when the chaotic neural network used in digital watermarking technology, it can not only quickly traverse all the pixels, and can quickly be stable output, but also easy to deal with large amounts of data, it is very feasible. Thus, the algorithm based on chaotic neural network of intelligent digital watermarking technology in this article comes up.
This paper presents the development of Digital Watermarking and chaotic neural network, and then introduces the basic concepts related to the watermark, Model of human visual system, wavelet transform theory, as well as the work of chaotic neural network theory. After introducing the design requirements of digital watermark, first describe how to determine the location of embedded watermark,and then, how to select the best watermark strength using chaotic neural network and program. Finally, assess the effect of image and reach a conclusion.
The subject of this thesis is focused on chaotic neural networks and intelligent, which is the point and the innovation distinguished from other digital watermarking algorithm. The previous algorithm mainly depends on the use of chaotic neural network, which is sensitive to the initial value of the watermark information to generate uniqueness, so an attacker can not predict the watermark to ensure the information security. When use the neural network model such as BP、Hopfield, it mainly make use of associative memory function to make blind check. In this paper, chaotic neural network is no longer starting from the above two points, but depending on its powerful search optimization features to look for the best embodiment of the watermark embedding strength with watermark invisibility and robustness, which is also the innovation different from other algorithm. The so-called intelligent because of the following reasons: With the development of digital watermarking technology, people have realized that a reasonable choice of the watermark embedding strength plays a vital role to the watermark algorithm. In the past, people select a fixed watermark embedding strength according to the earlier analysis and experimental experience, in recent years, one after another there have been some adjustment about algorithm for watermark embedding strength in accordance with image characteristics, but these algorithms need to re-testing different images and to update some of the parameters, required a great deal of calculation and do not have universal adaptability. In this paper, we use the combination of chaotic neural network and the spatial frequency which reflects the image quality, the changing transiently chaotic neural network energy function is set to negative values of images of the spatial frequency region, as long as the network will begin running, it will begin with chaotic state, and gradually running towards the direction of energy function decreases, when the network reached steady state, the output will be the best watermark embedding strength that the watermark image with the negative space reached minimize of the frequency (spatial frequency reached maximum value). We just need to test well the network parameters at the initial stage of experimental, we can get the final watermark image while any gray image input directly into the network adds a watermark. There's no need to calculate to determine the watermark embedding strength after analyzing the characteristics of different images, which shows the intelligence of the algorithm.
Similar with other watermarking algorithm,in this article selection the location of watermark embedding and watermark embedding strength determination are two key steps, But the method of selection is different. About selection the location of watermark embedding, calculation values of the host image after the wavelet decomposition of three-LL3, LH3, HL3, HH3 the matrix based on the DWT domain JND. And rewritten LL3, LH3, HL3, HH3 into a one-dimensional sequence, and in accordance with the size of the corresponding JND value reordering of sequences to be xulie_a, xulie_b, xulie_c, xulie_d; At the same time, obtain one-dimensional sequence in accordance with the result of watermark image after the wavelet decomposition level LL1, LH1, HL1, HH1 the matrix of wavelet coefficients descending sort to be the corresponding. Standing in the front of the wavelet coefficients are embedded in the host image, and this can cause big noise. Because the size of one-dimensional sequence after wavelet transform is 1 * 1024, embed watermark in the first 1024 points at each sequence of xulie_a, xulie_b, xulie_c, xulie_d. This 1024 points corresponds JND threshold, and more watermark information can be embedded, so corresponds with watermark image own better robustness. But watermark information should not be embedded in an unlimited amount, this will affect the invisibility of the watermark. The next key point is to determine the strength of watermark embedding. Chaotic neural network are Chaotic under the status of the energy function and its negative feedback, so network energy function is a critical operation. Setting energy function as negative of a watermark image with spatial frequency region. Network will be stabilized from running continuously through the rough from the chaos-based search to search based on Hopfield thin gradually. At the end, getting maximum output of the watermark image with spatial frequency, that is the best Watermark embedding strength. In order to verify the feasibility of the algorithm, in the experimental part of Chapter 5, we test the robustness in accordance with attacks that digital watermark often encounter. Experiment show that after watermark image with Gaussian noise, salt and pepper noise, shear, rotation, different JPEG quality factor of compression, low-pass, average, median filter, we Still able to extract watermark images from image. These images are more clearly visible on the subjective, Normalized correlation coefficient is above 0.8 objectively. Especially in the rotation and shear attack on the algorithm of the images and data are better than other algorithms. From the view of invisibility, the two sets images we received with a watermark image can not see the existence of watermark, and the values of NC are both above 0.9. On subjective and objective are entirely consistent with the requirements. Therefore, the algorithm is reasonably practicable.
数字水印按照隐藏的位置不同可以划分为空(时)域数字水印和变换域数字水印。在变换域数字水印技术中,由于小波变换的多分辨率特性与人眼视觉系统特性相似,因此本文选择在宿主图像小波变换的基础上嵌入水印图像。为了使含水印图像在鲁棒性和不可见性二者之间达到一个很好的折衷,本文设计将混沌神经网络的能量函数设置为含水印图像的区域内空间频率的负值,借助网络的搜索寻优特性,得到使含水印图像视觉效果最好(空间频率最大)的水印嵌入强度。水印嵌入强度由混沌神经网络确定,而非人们据经验或实验分析得到,体现了算法的智能性。实验过程中还要针对图像特点选择适合的嵌入区域,才能得到令人满意的含水印图像。最后针对实验所得到的结果图像进行常见的攻击,再提取水印进行分析,证明本文算法的可行性。
In the twenty-first century, Internet becomes unprecedented grand development. We can see the footprint of it at every corner in our daily life. It has advantage and disadvantage. The Internet was commendable but also digital media can easily be stolen, modified, copied and disseminated, with the attendant problem caused by information security, copyright issues and copyright disputes has become an increasingly serious social problem. Only depending on password protection can not completely effectively solve the problem, then digital watermarking technology comes up. Digital watermarking technology is an important branch of information hide technology, It is mainly through the media tag information embedded in the signal (digital watermark), Provide an effective technical means for media information in the field of anti-counterfeiting, tamper-resistant, certification, protection of data security and integrity.
Recently, digital watermarking technology has been a certain amount of development. It has a wide range of algorithm and implementation, but different algorithms have their own deficiencies, how to find an algorithm making the image embedded watermark in all aspects to achieve satisfactory results? How to get the target rapidly, accurately and clearly? Of course, we can continue to optimize the algorithm, or use some existing tools to achieve it. Chaotic neural network is a good instrument. Chaotic neural network not only has the characteristic of chaos such as randomness, regularity, ergodicity of the initial sensitivity and the characteristics of chaotic attractor, but also has the features of neural network such as with massively parallel processing, distributed information storage, self-adaptive self-organization and association study, when the chaotic neural network used in digital watermarking technology, it can not only quickly traverse all the pixels, and can quickly be stable output, but also easy to deal with large amounts of data, it is very feasible. Thus, the algorithm based on chaotic neural network of intelligent digital watermarking technology in this article comes up.
This paper presents the development of Digital Watermarking and chaotic neural network, and then introduces the basic concepts related to the watermark, Model of human visual system, wavelet transform theory, as well as the work of chaotic neural network theory. After introducing the design requirements of digital watermark, first describe how to determine the location of embedded watermark,and then, how to select the best watermark strength using chaotic neural network and program. Finally, assess the effect of image and reach a conclusion.
The subject of this thesis is focused on chaotic neural networks and intelligent, which is the point and the innovation distinguished from other digital watermarking algorithm. The previous algorithm mainly depends on the use of chaotic neural network, which is sensitive to the initial value of the watermark information to generate uniqueness, so an attacker can not predict the watermark to ensure the information security. When use the neural network model such as BP、Hopfield, it mainly make use of associative memory function to make blind check. In this paper, chaotic neural network is no longer starting from the above two points, but depending on its powerful search optimization features to look for the best embodiment of the watermark embedding strength with watermark invisibility and robustness, which is also the innovation different from other algorithm. The so-called intelligent because of the following reasons: With the development of digital watermarking technology, people have realized that a reasonable choice of the watermark embedding strength plays a vital role to the watermark algorithm. In the past, people select a fixed watermark embedding strength according to the earlier analysis and experimental experience, in recent years, one after another there have been some adjustment about algorithm for watermark embedding strength in accordance with image characteristics, but these algorithms need to re-testing different images and to update some of the parameters, required a great deal of calculation and do not have universal adaptability. In this paper, we use the combination of chaotic neural network and the spatial frequency which reflects the image quality, the changing transiently chaotic neural network energy function is set to negative values of images of the spatial frequency region, as long as the network will begin running, it will begin with chaotic state, and gradually running towards the direction of energy function decreases, when the network reached steady state, the output will be the best watermark embedding strength that the watermark image with the negative space reached minimize of the frequency (spatial frequency reached maximum value). We just need to test well the network parameters at the initial stage of experimental, we can get the final watermark image while any gray image input directly into the network adds a watermark. There's no need to calculate to determine the watermark embedding strength after analyzing the characteristics of different images, which shows the intelligence of the algorithm.
Similar with other watermarking algorithm,in this article selection the location of watermark embedding and watermark embedding strength determination are two key steps, But the method of selection is different. About selection the location of watermark embedding, calculation values of the host image after the wavelet decomposition of three-LL3, LH3, HL3, HH3 the matrix based on the DWT domain JND. And rewritten LL3, LH3, HL3, HH3 into a one-dimensional sequence, and in accordance with the size of the corresponding JND value reordering of sequences to be xulie_a, xulie_b, xulie_c, xulie_d; At the same time, obtain one-dimensional sequence in accordance with the result of watermark image after the wavelet decomposition level LL1, LH1, HL1, HH1 the matrix of wavelet coefficients descending sort to be the corresponding. Standing in the front of the wavelet coefficients are embedded in the host image, and this can cause big noise. Because the size of one-dimensional sequence after wavelet transform is 1 * 1024, embed watermark in the first 1024 points at each sequence of xulie_a, xulie_b, xulie_c, xulie_d. This 1024 points corresponds JND threshold, and more watermark information can be embedded, so corresponds with watermark image own better robustness. But watermark information should not be embedded in an unlimited amount, this will affect the invisibility of the watermark. The next key point is to determine the strength of watermark embedding. Chaotic neural network are Chaotic under the status of the energy function and its negative feedback, so network energy function is a critical operation. Setting energy function as negative of a watermark image with spatial frequency region. Network will be stabilized from running continuously through the rough from the chaos-based search to search based on Hopfield thin gradually. At the end, getting maximum output of the watermark image with spatial frequency, that is the best Watermark embedding strength. In order to verify the feasibility of the algorithm, in the experimental part of Chapter 5, we test the robustness in accordance with attacks that digital watermark often encounter. Experiment show that after watermark image with Gaussian noise, salt and pepper noise, shear, rotation, different JPEG quality factor of compression, low-pass, average, median filter, we Still able to extract watermark images from image. These images are more clearly visible on the subjective, Normalized correlation coefficient is above 0.8 objectively. Especially in the rotation and shear attack on the algorithm of the images and data are better than other algorithms. From the view of invisibility, the two sets images we received with a watermark image can not see the existence of watermark, and the values of NC are both above 0.9. On subjective and objective are entirely consistent with the requirements. Therefore, the algorithm is reasonably practicable.
引文
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[2]Tirkel A Z,et al . electronic watermark[J] . Digital image computing,Technology and Applications,1993,Macquarie University,1:666-673.
[3]Schyndel R G van,Tirkel A Z,Osborne C F.A digital watermark[J].First IEEE International Image Processing Conference,1994,2:86-90.
[4] Cox I J,et al . Secure spread spectrum watermarking for multimedia[J].Technical Report 95-10,NEC Research Institute,Princeton,NJ,1995.
[5]Pitas I.A method for signature casting on digital images[J].IEEE International Conference on Image Processing,1996,3:215-218.
[6]Voyatzis G,Pitas I.Embedding robust watermarks by chaotic mixing[J].13th International Conference on Digital Signal Processing,1997,1:213-216.
[7]Kundur D,Hatzinakos D.Digital watermarking for telltale tamper-proofing and authentication[J].Proceedings of the IEEE Special Issue on Identification and Protection of Multimedia Information,1998,87(7) .
[8]何迪,何晨,蒋铃鸽等.一类折叠次数无限的混沌自映射及其应用[J] .上海交通大学学报,2001, 35(1),10-12.
[9] Martin T. Hagan, Howard B. Demuth, Mark Beale.神经网络设计(英文版)[M].北京:机械工业出版社,中信出版社,2002.
[10]戴一旻.改进的自适应暂态混沌神经网络及其在CDMA多用户检测中的应用[D].上海:上海交通大学,2003.
[11]Christine I,Edward J.Digital Watermarking[J].Algorithms and applications,IEEE Signal Processing Magazine,2001(7):33-46.
[12]刘利田.一种小波域数字水印算法[D].南京:南京信息工程大学硕士论文,2007,9:9-11.
[13] Cox I J,Leighton F T.Secure Spread Spectrum watermarking forMultimedia[J].IEEE Trans on Image Processing,1997,6(12):1673-1687.
[14]Ingemar J.Cox,Joe Killian,F,Thomson,and Talal Shamoon.Secure Spread Spectrum watermarking for Multimedia[J].IEEE Trans.on Image Processing, 1997,6(12):1673-1687.
[15]G.Depovere,T.Kalker,and J.P.Linnartz.Improved watermark detection using filtering Before Correlition[J] . IEEE Int . Conf . Image Processing, 1998,1(10):430-434.
[16]Lin Chingyung,Wu min,Bloom J A,et al.Rotation,Scale and Translation Resilient Watermarking for Images[J].In:IEEE Trans.on Image Processing,2001,10(5):767-782.
[17]Y.Wang,J.Doherty,R.Van Dyck.A Novel Wavelet-based Algorithm for Watermarking Digital Images[J].Proc.CISS 2000.2000(1):7-12.
[18] C.Lin,et al Rotation.Scale and Translation Resilient Public Watermarking for Images[J].Proc,SPIE,2000.1,3971(2):90-98.
[19]于长辉,陈良海.基于DCT和NN的数字水印技术的研究[J].微计算机信息.2008,24(9-3):73-77.
[20]贺仁亚,程乾生.一种用于认证的小波变换域的数字水印技术[J] .计算机辅助设计与图形学学报,2001 ,13(9) :812 - 815.
[21]Friedman.G.L.The trustworthy digital camera: Restoring credibility to the photographic image[J] . IEEE Trans. On Consumer Electronics,1993, 39(4):404-408.
[22] Van Schydel R.G., Tirkel A.Z. and Osborne C.F. A digital watermark[J] . IEEE International Conference on Image Processing, Austin,Texas,USA,1994, 2:86-90.
[23] Yeung M.M. and Mintzer F.An invisible watermarking technique for image verification[J] . Proc. IEEE Int.Conference on Image Processing, Santa Barbara,California,1997, 2:680-683.
[24] Fridrich J.Goljan M.and Memon N.Further attacks on Yeung-Mingtzer watermarking scheme[J] .Proc. SPIE:Security and Watermarking of MultimediaContents,San Jose,California,Jan.2000:428-437.
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