Data hiding and watermarking in JPEG compressed domain by DC coefficient modification

Multiresolution Watermarking for Digital ImagesMultiresolution Watermark Based on Wavelet Transform forDigital imagesByVallabha VHCranes Software International LimitedIntroductionThe rapid expansion of the Internet in the past years has rapidly increased the availability of digital data such as audio, images and videos to the public. As we have witnessed in the past few months, the problem of protecting multimedia information becomes more and more important and a lot of copyright owners are concerned about protecting any illegal duplication of their data or work. Some serious work needs to be done in order to maintain the availability of multimedia information but, in the meantime, the industry must come up with ways to protect intellectual property of creators, distributors or simple owners of such data. This is an interesting challenge and this is probably why so much attention has been drawn toward the development of digital images protection schemes. Of the many approaches possible to protect visual data, digital watermarking is probably the one that has received most interest. The idea of robust watermarking of images is to embed information data within the image with an insensible form for human visual system but in a way that protects from attacks such as common image processing operations. The goal is to produce an image that looks exactly the same to a human eye but still allows its positive identification in comparison with the owner's key if necessary.This paper attempts to first introduce general idea behind digital watermarking as well as some of its basic notions. It is followed by describing some applications of watermarking techniques and the difficulties faced in this new technology. The paper ends with an overview on some copyright protection techniques, involving watermarking and it seen as to why digimarks have become an important research subject of late.1 Basis on watermarkingThe increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. Digital watermarking is a method that has received a lot of attention in the past few years. A digital watermark can be described as a visible or preferably invisible identification code that is permanently embedded in the data . It means that it remains present within the data after any decryption process. A general definition can be given: !"Hiding of a secret message or information within an ordinary message and theextraction of it at its destination. !"Complementary to encryption, it allows some protection of the data after decryption. As we know, encryption procedure aims at protecting the image (or other kind of data) during its transmission. Once decrypted, the image is not protected anymore. By adding watermark, we add a certain degree of protection to the image (or to the information that it contains) even after the decryption process has taken place. The goal is to embed some information in the image without affecting its visual content. In the copyright protection context, watermarking is used to add a key in the multimedia data that authenticates the legal copyright holder and that cannot be manipulated or removed without impairing the data in a way that removes any commercial value . In Figure 1 a general watermarking scheme in order to give an idea of the different operations involved in the process.The first distinction that one needs to do in the study of watermarking for digital images is the notion of visible watermarks versus invisible ones. The first ones are used to mark, obviously in a clearly detectable way, a digital image in order to give a general idea of what it looks like while preventing any commercial use of that particular image. The purpose here is to forbid any unauthorized use of an image by adding an obvious identification key, which removes the image’s commercial value. On the other hand, invisible watermarks are used for content and/or author identification in order to be able to determine the origin of an image. They can also be used in unauthorized image’s copies detection either to prove ownership or to identify a customer. The invisible scheme does not intend to forbid any access to an image but its purpose is to be able to tell if a specified image has been used without the owner’s formal consent or if the image has been altered in any way. This approach is certainly the one that has received the most attention in the past couple years . In that line of thoughts, it is possible to differentiate two ways of embeddinginvisible information in a digital image:1.1 In the spatial domain.The first watermarking scheme that was introduced works directly in the spatial domain. By some image analysis operations (e.g. Edge detection), it is possible to get perceptual information about the image, which is then used to embed a watermarking key, directly in the intensity values of predetermined regions of the image. Those pretty simple techniques provide a simple and effective way for embedding an invisible watermark into an original image but don’t show robustness to common image alterations .1.2 In the transform domain.Another way to produce high quality watermarked image is by first transforming the original image into the frequency domain by the use of Fourier, Discrete Cosine or Wavelet transforms for example. With this technique, the marks are not added to the intensities of the image but to the values of its transform coefficients. Then inverse-transforming the marked coefficients forms the watermarked image. The use of frequency based transforms allows the direct understanding of the content of the image; therefore, characteristics of the human visual system (HVS) can be taken into account more easily when it is time to decide the intensity and position of the watermarks to be applied to a given image.2 Applications & Requirements of Invisible WatermarksAs previously stated, watermarking can be really useful in several areas of interest involving digital images. In order to fully understand the main challengesinvolved in the development of watermarking-related tools, some , some applications of invisible watermarks are listed hereto;2.1 Fingerprinting:In order to trace the source of illegal copies the owner can embed different watermarking keys in the copies that are supplied to different customer. For the owner, embedding a unique serial number-like watermark is a good way to detect customers who break their licence agreement by copying the protected data and supplying it to a third party.2.2 Indexing:Watermarking offers a wide range of new capabilities to multimedia applications. It allows the indexing of video mail by permitting the insertion of comments in video content as well as the indexing of movies or news items by making available the utilization of markers that can be exploited in search engines. As the number of images and video contents online increases a lot faster than the capabilities of today’s search engine, it is important to plan ahead for new ways to allow quick access to multimedia data and watermarking is certainly a promising way to do so.2.3 Copyright Protection & Owner identification:To protect its intellectual property, the data owner can embed a watermark representing copyright information of his data. This application can be a really helpful tool in settling copyright disputes in court. It is probably the most widely spread use of digital images watermarking and it is also the application we have worked on in the present project.2.4 Broadcast monitoring:In order to help the automated identification of broadcasted programs, original watermarks can be inserted in any type of data to be widely broadcasted on a network. It could assure that advertisers received the airtime they have paid for or make certain that musicians’ property is not rebroadcast by pirate stations (or at least, if so, that it can be detected).2.5 Copy protection:The watermarked information can directly control digital recording device. The embedded key can represent a copy-permission bit stream that is detected by the recording device which then decide if the copying procedure should go on (allowed) or not (prohibited).2.6 Data Authentication:Fragile watermarks are used to detect any corruption of animage or any other type of data. If the watermark is detected, the data is genuine,if not, the data has been corrupted and cannot be considered.2.7 Data Hiding (Covert Communications):The transmission of private data is probably one of the earliest applications of watermarking. As one would probably have already understood, it consists of implanting a strategic message into an innocuous one in a way that would prevent any unauthorized person to detect it.2.8 Medical Safety:Embedding the date and patient’s name in medical images could increase the confidentiality of medical information as well as the security.From the applications mentioned above, one can divide watermarks in two distinct types:• Robust, for the first five applications and• Fragile for the last three.As the name suggests, the fragile watermarks are meant to disappear if the image (or other data used) is corrupted. It can be really useful in judiciary process for example where it is imperative to be certain that what is used is genuine. In contrast, robust watermarks are designed to resist attacks (malicious or not) in order for the key to still be detectable after it has undergone them. The range of applications, which includes copyright protection , asks for very different specific characteristics. Nevertheless, it is possible to list a set of basic requirements for robust watermarks.Those are a generalization but should allow the reader to understand, once again, the main difficulties involved in the design of digimarks systems and should also act as natural preliminaries to the following paragraph discussing the difficulties of that technology;3.1 Perceptual Transparency:Use characteristics of the HVS to assure that the watermark is not visible under typical viewing conditions . Basically, it means that a watermarked image should not seem any different from the original(unwatermarked)one; i.e. one should not notice any degradation in the perceived quality. Other types of watermarks are meant to be visible but in most application they are not and this is why we treat transparency as a basic requirement of digital watermarking.3.2 Robustness:Watermark still can be detected after the image has undergone attacks (malicious or not ) compression, halftoning, etc .Ideally, the amount of image distortion necessary to remove the watermark should degrade the desired image quality to the point of becoming commercially valueless .3.3 Capacity:Allow insertion of multiple, independently detectable watermarks in an image .3.4 Payload of watermark:That represents the amount of information that can actually be stored in a particular data stream. This requirement is highly dependent on the host medium, the intended application as well the quality aimed for. By the addition of the year of copyright, the permissions granted on the work and the rating for it, the total number of bits to be embedded should be from 60 to 70 bits. In the design of watermarking scheme, some attention must be given to the fact that this is probably the minimal amount of information that one wants to embed, therefore it must be made sure that the host data will be able to support it while keeping good perceptual transparency.3.5 Security: The basis of watermarking security should lie on Kerckhoff’s assumption that one should assume that the method used to encrypt the data is known to the unauthorized party . It means that watermarking security can be interpreted as encryption security leading directly to the principle that it must lie mainly in the choice of the embedded key.3.6 Specificity: W atermark should be universal, i.e. applicable to images as well as audio and video media . In fact, that has been found not to be true; the general concept might be the same in multiple applications but, in watermarking, one size does not fit all .As it might already be obvious for an attentive reader, a lot of difficulties are encountered while trying to define the ideal watermarking scheme for a particular application.• Some particular attention must be paid in order not to downgrade the quality ofMultiresolution Watermarking for Digital Imagesthe source image too much by the insertion of digital watermark (i.e.Transparency requirement).• Digital watermarking algorithms usually use the lower-order bit planes of the original image but so do intentional disturbance algorithms.The availability of watermark readers allows copyright thieves to determine if the watermark exists in the image or not.• In highly compressed JPEG images, there is a limited amount of data that can be used to insert digital watermarks.• The introduction of artifacts (such as blockiness) by compression techniques usually destroys watermarks easily.• Robustness and transparency are two fundamentally opposed requirements; a tradeoff between the two must then be made.Those form only a brief overview of some problems involved in digital watermarking technologies. Of course, as in any area of research, unexpected troubles always arise either with the underlying principles or during the technical evolution of the concept.4 Attacks on WatermarksThere are several kinds of malicious attacks , which result in a partial or even total destruction of the embed identification key and for which more advanced watermarking scheme should employed;4.1 Active attacks: Here, the hacker tries deliberately to remove the watermark or simplymake it undetectable. This is a big issue in copyright protection, fingerprinting or copy control for example.4.2 Passive attacks: In this case, the attacker is not trying to remove the watermark but simply attempting to determine if a given mark is present or not. As the reader should understand, protection against passive attacks is of the utmost importance in covert communications where the simple knowledge of the presence of watermark is often more than one want to grant.4.3 Collusion attacks: In collusive attacks, the goal of the hacker is the same as for the active attacks but the method is slightly different. In order to remove the watermark, the hacker uses several copies of the same data, containing each different watermark, to construct a new copy without any watermark. This is a problem in fingerprinting applications (e.g. In the film industry) but is not the widely spread because the attacker must have access to multiple copies of the same data and that the number needed can be pretty important.4.4 Forgery attacks: This is probably the main concern in data authentication. In forgery attacks, the hacker aims at embedding a new, valid watermark rather than removing one. By doing so, it allows him to modify the protected data as he wants and then, re-implants a new given key to replace the destructed (fragile) one, thus making the corrupted image seems genuine.Multiresolution Watermarking for Digital ImagesThose four types of malicious attacks are only a categorization of what has been encountered in the past. Of course, a lot of new attacks can be designed and it is impossible to know what will come out next from hackers’ imagination. To those malicious attacks, one must add all signal processing operations involved in the transmission or storage of data, which can naturally degrade the image and alter the watermarked information to the point of not being detectable anymore. In fact, the identification and classification of attacks, as well as the implantation of a standard benchmark for robustness testing is of great importance and will be a key issue in the future development of watermarking.5 DescriptionLet us discuss an example on watermarking method proposed by Ward . This first generation technique called Multiresolution Watermark for Digital Images works in the wavelet domain . The modus operandi is pretty simple since it consists of adding weighted pseudo-random codes to the large coefficients at the high and medium frequency bands of the discrete wavelet transform of an image. Even if it might first appear theoretically straightforward the actual implementation is a little more complicated than it seems and great effort will be made here to clarify it. First of all, one must know why the wavelets approach is used here. The characteristics of this transform domain are well suited for masking consideration since it is well localized both in time and frequency. Besides wavelet transforms match the multi channel model of the HVS so one can actually set a numerical limit to the wavelets coefficients alteration in order to stay under the HVS just noticeable difference 3 (JND), for which our eyes start to become aware of the modifications in the image . Besides, wavelet transforms is a part of upcoming compression standards (such as JPEG-2000) so wavelet-based techniques would allow a much easier and optimized way to include a copyright protection device in the compression code itself .A second important aspect of the technique used here is the way to introduce a watermark in an image. A watermark should be introduced in perceptually significant regions of the data in order to remain robust. However, by doing so, we risk to alter the image (i.e. perceivably). The technique described here follows to a certain degree this requirement but tries to make the introduced watermark as invisible as it can while showing good robustness. As the reader will understand, we have chosen to ensure transparency of the watermark and, in the same time keep the robustness by embedding the information within only the high and medium frequencies while keeping third resolution low frequencies, for which the HVS is most sensitive, untouched. The implemented technique uses nonoblivious watermark. In copyright protection, it is reasonable to assume that the owner or the user who wants to verify the presence of a particular watermarking key has access to the original unwatermarked image. This type of watermarking scheme is called nonoblivious (or private) watermarking in opposition to oblivious (or public) techniques, such as copyMultiresolution Watermarking for Digital Imagesprotection, where the original image is not accessible.First, the multiresolution and hierarchical approach save computational load in detection. Then, it allows good transparency because it uses characteristics of our visual system. As we know, the HVS is more sensible to small perturbation in the lower frequency bands thus keeping intact the low frequencies assures that the watermarked image will be as close as possible to the original one. It has to be noted that, from a signal processing point of view, the requirements of transparency and robustness conflict each other. The developed method is well suited because it allows a certain tradeoff between the two. Finally, and as already stated, the use of wavelet transform matches the upcoming image/video compression standards, which mean that such a watermarking scheme could easily be included as a part of JPEG-2000 or other wavelet-based standard. Before proceeding with the description of encoding - decoding scheme, let us revisit the goals we had set at the beginning of the paper. In the first place, we were willing to implement a perceptual watermarking scheme for digital images based on wavelet transforms . Then we wanted to test the robustness of our implementation to different type of attacks such as common image processing operations.5.1 WatermarkerThe first part of the watermarking process is, of course, the encoder. The first step is to decompose the image into ten frequency bands using three resolutions of Haar wavelets. Figure 2’s bank of filters represents well the idea of the octave-band structure of Haar wavelets, which gives pyramid structurefrequency localization as shown in Figure 3.Multiresolution Watermarking for Digital ImagesBefore, we go on, we must point out that an undersampling operation is done after every filtering.It must be understood that the choice of the Haar wavelet in our system was one made for simplicity. However, we had in mind to investigate the influence of the choice of the wavelet function in our results but, in order to test the robustness truthfully, we had to give up the idea in favor of the addition of extra robustness testing procedures. The next operation is to add a pseudo random sequence N , in fact a Gaussian distribution of mean zero and variance one, to the coefficients of the medium and high frequency bands (i.e. all the bands except the lowest one which is represented by the top left corner in Figure 3). The normal distribution is used because it has been proven to be quite robust to collusive attacks . In order to weight the watermark according to the magnitude of the wavelet coefficients, we used one of the two following relations between the original coefficients y and ÿ , the ones containing the watermark:ÿ[m,n] = y[m,n] + alpha (y[m,n])2 N[m,n] ( 1 )&ÿ[m,n] = y[m,n] + alpha . abs(y[m,n])N[m,n] ( 2 )It must be pointed out that the relations (1) and (2), even though they are mathematically different, have the exact same goal which is to put more weight to the watermark added to high value wavelet coefficients. The parameter alpha is to control the level of the watermark; it is in fact a good way to choose between good transparency or good robustness or a tradeoff between the two. Finally, the two-dimension inverse wavelet transform of is computed to form ÿ the watermarked image. Figure 4 gives a good idea of the main components of the encoder that we have implemented for our project.Multiresolution Watermarking for Digital Images5.2 DecoderAt the other end of the communication channel, a decoder is used to extract the watermarked information from the received image. Upon reception of the supposedly watermarked image, the algorithm first isolates the signature included in this image by comparing the DWT coefficients of the image with those of the original (non-watermarked) one.The following operation consists of taking the identified key to put in contrast with the found signature by computing the crosscorrelation at the first resolution level (i.e. highest frequency coefficients). The watermark is called detected if there is a peak in the crosscorrelation corresponding to a positive identification. If there is no central peak, the decoder adds the second resolution level (i.e. the bottom left square in the pyramid structure of Figure 3) to the computation aiming at finding for a peak. Once again, if there is a peak, the watermark is called detected and if not, we go to the third resolution… and so on until we reach the ninth resolution limit. The main advantage of this technique is that while allowing good detection, even in the presence of corruption, it keeps the level of false positive detection to a minimum since the found signature has to go through detection step of positive identification to be called detected. The detector step aims at ensuring the maximum exactitude in the detection of the owner identification key and, as said previously,minimizing the number of false positive detection. The results presented later on should convince the reader of the performance of our decoder.6 Robustness TestingWe’ll focus only on the result of attacks due to widely spread image processing operations for simplicity. By doing so, we make sure that our system could be used in the transmission of images. Since this is an introduction to the watermarking subject, this gives a general idea of the kind of problems involved in the process and see how simple image processing operations can affect the functioning of a watermarking method. Furthermore, it must be pointed out that, one of the main problems to be solved in the field of digital watermarking is the absence of general testing benchmark. Such examination procedure has to be implemented and generalized in order to be able to compare different watermarking techniques. Nevertheless, in order to test the robustness of any implementation, here are 3 testing procedure. The characteristics of chosen attacks can be summarized as follow:6.1 JPEG: Introduces blocking artefacts in the compressed images.6.2 Halftoning: It is used for printing; it gives the impression of grey levels while,in fact, there are only two levels (black & white).6.3 Filtering: Of course, a lot of different kinds of filter are used in all areas ofsignal processing. As samples, we have used low pass, average and unsharp filters.Figure 6 Attacks EncounteredI have tested the watermarking algorithm on MATLAB 6.5 using Image and Wavelet Toolboxes.MATLAB was very helpful in testing and optmizing the algorithm.It helped me in avoiding writing codes for wavelets as they are optimally implemented in Wavelet Toolbox.I’d like to thank the Mathworks Development team which has provided such wonderful and powerful tools for testing algorithms.References[1] I J Cox , M L Miller and J A Bloom , Watermarking Applications and their Properties .[2] A H Paquet , R K Ward , Wavelet Based Digital Watermarking[3] G Voyatzis and I Pitas , The Use Of Watermarks in theProtection of Digital Multimedia Products .[4] H Y Lo , S Topiwala and J Wang , Wavelet BasedSteganography and Watermarking .。

一种鲁棒的数字水印算法龚成清【摘要】数字水印是信息隐藏和版权保护的有效手段。

针对一般数字水印算法的视觉性和鲁棒性无法兼顾的问题，对JPEG 2000的图像格式提出了一种自适应的盲水印算法。

算法使用m+n位的线性反馈移位寄存器对水印图像进行移位置乱，然后利用JPEG 2000图像的特点，对原始图像进行小波变换处理后选择低频子带进行水印的嵌入，在量化处理的同时完成了水印的嵌入，提高了水印嵌入的速度。

根据LSFR的性质，算法对水印进行有效地检测和移位复原，实现了水印的盲提取。

实验表明，该算法具有良好的视觉性和抵抗攻击的鲁棒性。

%Digital watermarking is an effective means of information hiding and copyright protection. For general digital watermarking algorithm can not balance the visuality and robustness,this paper proposes a robust blind watermarking algorithm of JPEG2000 images.It designes a Linear Shift Feedback Register of m+n bits to scramble the watermark image,then choses the low frequency which is processed after the wavelet transform to embed the watermark bit.It completes the watermark embedding with the quantification processing.It improves the watermark embedding speed. According to the nature of LSFR, the watermark is detected and to be shifted to restore the watermark image.So it achieves blind watermark extraction.Experiments show that, the algorithm has a good visuality and robustness.【期刊名称】《齐齐哈尔大学学报（自然科学版）》【年(卷),期】2014(000)005【总页数】6页(P10-15)【关键词】水印;量化;移位;鲁棒性【作者】龚成清【作者单位】广东女子职业技术学院应用设计系，广州 511450【正文语种】中文【中图分类】TP391.9数字水印技术广泛用于图像、视频和音频作品中的信息隐藏和版权保护。

音频数字水印技术研究所示。

仿真工具为Ｍａｔｌａｂ６，５。

硬件测试环境为奔腾４．２．ＯＭＨｚ２５６ＲＡＭ。

取音频每帧的长度为５１２。

水印相似性判别阈值ｒ＝Ｏ．５。

３．４．１隐形性测试将原始信号嵌入不同强度的水印计算含水印信号的信噪比，如式（３－１５）∑ｓ２㈣册ｒ２１０１０函。

豇南ｉ研（３－１５）ｓ倒为原始音频信号，，为含水印信号，聆为样点数。

实验结果如表３．１，嵌入水印后音频信号（嵌入强度为５ｄＢ）如图３．６。

表３．１不同强度水印对信号的影响图３．４原始的音频信号（采样点数ｘ１０５）图３．５原始水印图象１Ｉｔ３．６嵌入强度５ｄＢ水印后的音频号（采样点数ｘ１０５）工程硕士学位论文通过表３．１可知当嵌入强度小于２ｄＢ时水印的隐蔽性很好，对音质基本没有影响。

图３．４、图３．６可以看出当嵌入强度为５ｄＢ时，原信号与含水印信号有细微差别，经过主观的听觉测试也可发现微弱的杂音，说明随着嵌入的强度的增强水印的隐蔽性逐渐降低。

３．４．２鲁棒性测试实验方法为对含水印的信号（水印嵌入强度为２ｄＢ）进行各种攻击，然后提取水印，检查提取水印的正确率Ｆ，正确率的计算方法为式（１５）。

Ｆ＝∑ｇ弼何，唰ｘ１００％肿删＝世非？；Ｄｇ＜上（３－１６）口＝Ｏｗ为嵌入的原始一维二进制序列，耽为提取的一维二进制序列，它们的长度为三。

进行的鲁棒性实验如下，各种攻击后提取水印的正确率和主观感受如表３．２。

１．无攻击。

在无任何攻击的情况下提取水印如图３．７（ａ）。

２．加入高斯白噪声。

在信噪比为４０ｄＢ的白噪声攻击后提取的水印如图３．７（ｂ）。

在信噪比２６ｄＢ的白噪声攻击后提取的水印如图３．７（ｃ）。

３．加入有色噪声。

有色噪声又叫带通噪声，既在某个频带上信号的能量突然变大。

在加入信噪比为４０ｄＢ的有色噪声攻击后提取的水印如图３．７（ｄ）。

４．低通滤波。

将不同嵌入强度含水印信号分别通过截止频率为３ｋＨｚ和４ｋＨｚ的低通滤波器（采用的工具是ＣｏｏｌＥｄｉｔ），检测出的水印如图３．７（ｅ）和图３．７（ｆ）５．重新量化。

JPEG图像版权保护水印算法蒋铭1,2，马兆丰1,2，芦效峰1,2，钮心忻1，杨义先11 (北京邮电大学网络和信息攻防技术教育部重点实验室，北京 100876)2 (北京国泰信安科技有限公司,北京100086)摘要提出了一种实用的基于水印算法的JPEG图像版权保护方法。

对JPEG图像解压缩后的像素矩阵进行操作处理，水印嵌入在分块图像的DCT中低频交流系数。

对水印信号进行了伪随机置乱，结合了视觉掩蔽特性控制水印嵌入的强度，并引入了量化条件分析处理避免JPEG压缩对水印的影响。

水印提取不需要载体JPEG图像。

实验结果表明，算法对于常见的图像处理方法和恶意攻击具有良好的鲁棒性，特别是图像格式转换等处理，有利于JPEG图像的版权保护。

关键词JPEG图像; 数字水印; 视觉掩蔽; 版权保护JPEG Image Watermarking Algorithm for Copyright ProtectionJiang Ming1,2, Ma Zhao-feng1,2, Lu Xiaofeng1,2, Niu Xin-xin1, Yang Yi-xian11(Key Laboratory of network and information attack & defence technology of MOE, Beijing University of Posts andTelecommunications, Beijing 100876, China)2(Beijing National Security Science and Technology Co..Ltd, Beijing ,100086, China)Abstract An innovative practical watermarking algorithm for the copyright protection of JPEG image is proposed in this paper. By operating the pixel matrix after depressing the JPEG image directly, the DCT AC coefficient of block image is used to embed the watermark. In this embedding process, Scrambling the watermark first, controlling the watermark strength by visual masking and analyzing the quantization condition to avoid the distortion for watermark. No original image is needed when extracting the watermark. The experimental results show the proposed scheme can achieve good excellent robustness to common image processing methods and some common attacks, such as image format conversion especially in favor of protecting the copyright of JPEG image.Keywords JPEG image; digital watermark; visual masking; copyright protection1 引言随着计算机和网络通信技术的飞速发展，互联网为数字媒体的传播和电子商务提供了极为方便的途径。

基于预测差值的医学图像可逆信息隐藏范鑫惠; 李辉【期刊名称】《《北京化工大学学报（自然科学版）》》【年(卷),期】2019(046)002【总页数】7页(P83-89)【关键词】可逆信息隐藏; 医学图像; 中值边缘预测; 预测差值【作者】范鑫惠; 李辉【作者单位】北京化工大学信息科学与技术学院北京100029【正文语种】中文【中图分类】TP391引言信息隐藏是将受保护的信息嵌入到载体中,可逆信息隐藏技术是其中一个重要的分支,不仅能完整提取嵌入信息,同时可无损恢复载体。

近年来随着医疗数字化进程的推进,海量的医学图像在网络上传播,传输过程中任何的误差都有可能导致误诊,而且如果在医学图像中嵌入了病人的诊断信息,则需要很高的嵌入量。

普通的嵌入算法在嵌入大量信息的同时,会不可避免地使解密后的图像出现一定程度的失真。

因此,对于具有高嵌入量和高精度要求的医学图像,需采用可逆信息隐藏算法,保证原图像和解密后图像的一致性。

目前的可逆信息隐藏方法主要有3 类：无损压缩[1-4]、图像直方图平移[5-7]和差值扩展[8-11]。

基于无压缩的可逆算法实现简单,但在嵌入容量上不具有优势而且容易带来图像失真；基于直方图的可逆算法虽然保证了图像精度,但嵌入量仍达不到令人满意的效果。

因此研究者开发了利用相邻像素之间的相关性嵌入秘密信息的差值扩展技术[8],在传统的差值扩展技术的基础上,提出了基于预测差值的可逆算法。

张丽娜[9]采用平均值预测算法生成预测差值,利用待预测像素点周围的8 个像素进行预测,预测准确度有所提高,但嵌入量减小；Hong等[10]采用中值边缘预测（median edge detection prediction,MPE）算法生成预测差值,再根据预测差值的分类对像素点进行平移或保持不变,虽然提高了嵌入量,但未对像素值为0 和255 的像素点进行操作,仍不能满足医学图像高嵌入量的要求。

本文基于文献[10]中的MPE 算法,结合医学图像的特点,提出一种基于预测差值的医学图像可逆信息隐藏算法。

视觉与听觉信息处理国家重点实验室视觉与听觉信息处理国家重点实验室、智能科学系（信息科学中心）主任：查红彬副主任：吴玺宏谢昆青刘宏视觉与听觉信息处理国家重点实验室学术委员会视觉研究室视觉信息处理研究室在充分发挥学科交叉的综合优势基础上，特别注意从视觉信息处理中提出新的数学问题，进一步发展用于信息处理的数学方法，主要研究方法包括图像压缩与编码、图像处理和模式识别、计算机视觉等。

多年来我们承担了国家科技攻关、863高科技、攀登计划、自然科学基金、博士点基金等大量课题，同时取得了一批有独创性的重大成果。

本研究室具体包含如下4个研究领域：1、图像处理、图像理解和计算机视觉2、生物特征识别3、图像的压缩、复现、传输及其信息安全4、三维视觉计算与机器人(New!!!)联系人：张超电话: 62757000视觉信息处理研究室科研方向:[1]图像处理、图像理解和计算机视觉[2]生物特征识别[3]图像的压缩、复现、传输及其信息安全[4]三维视觉计算与机器人视觉信息处理研究室科研项目：用于患者行为实时跟踪的护理机器人主动视觉研究，国家自然科学基金项目，2002-2004建筑物与复杂场景三维数字化技术的基础研究，教育部科学技术研究重点项目，2003-2005数字博物馆关键技术研究，北京大学15-211项目，2003-2005Document Image Retrieval，与日本Ricoh Co.的合作项目，2003-20043D Model Retrieval，与日本Fujitsu研发中心的合作项目，2003-2004数字几何处理的理论框架与关键技术研究，国家自然科学基金重点项目，2004-2007计算机视觉研究，自然科学基金重大项目子项小波分析理论及其在图像处理中的应用，自然科学基金跨学部重点项目数学机械化与自动推理平台"课题"信息安全、传输与可靠性研究，国家重点基础研究发展规划项目微阵列基因表达数据分析和可视化研究，自然科学基金项目全国优秀博士学位论文奖励基金， 2001-2005JPEG2000图象压缩集成电路核心技术研究，国家863高科技研究项目基于小波的视频压缩与通讯系统研究，国家973重大基础项目的子专题符合JPEG2000的图象压缩集成电路研究，教育部骨干教师项目多进制小波理论及其在基于内容的图像压缩中的应用，国家自然科学基金项目， 2004.1-2006.12 视觉研究室主要人员:听觉研究室言语听觉信息处理研究室成立于1988年，多年来一直以人工神经网络、智能机器学习等为理论基石，以听觉信息处理、语音信号处理和语言信息处理为研究背景进行了深入的理论探索和实践研究。

第45卷第3期包装工程2024年2月PACKAGING ENGINEERING·193·空频域结合的多尺度扩张卷积注意力数字水印孙刘杰，刘磊（上海理工大学，上海200125）摘要：目的将深度学习应用于数字水印，在隐藏信息的同时，不断提高图像的不可见性和鲁棒性，提出一种结合空间域和频率域的多尺度扩张卷积注意力数字水印算法（SF-ACA）。

方法SF-ACA算法的网络框架包含由ACA和SFE构成的生成器、解码器2个部分组成。

其中，ACA网络中的MCA模块将3个不同扩张率的扩张卷积对载体图像以多尺度融合的方式进行特征提取，使载体图像能更有效地隐藏水印信息；SFE结合快速傅里叶卷积块，在空域和频域中通过不同大小的感受野捕获互补信息，更精准地获取水印的特征信息，增强了秘密信息的不可见性和鲁棒性。

结果本文提出的水印方法在隐藏与载体图像尺寸相等的三通道彩色图像时，PSNR值为38.81 dB，较UDH方法的PSNR值提高了7.78%。

水印图像的隐藏容量是4 096比特，该算法与UDH方法在Dropout、Gaussian噪声、JPEG攻击下，提取精度分别提升了5.38%、10.5%、1.65%，满足不可见性要求的同时实现了强鲁棒性。

结论本文方法在隐藏容量较大时，不可见性和鲁棒性都达到了较好的性能。

关键词：深度学习；水印；注意力机制；扩张卷积；傅里叶变换中图分类号：TB486；TP391 文献标志码：A 文章编号：1001-3563(2024)03-0193-08DOI：10.19554/ki.1001-3563.2024.03.022Digital Watermarking Combining Spatial Domain and Frequency Domain Based onMulti-scale Expanded Convolutional AttentionSUN Liujie, LIU Lei(University of Shanghai for Science and Technology, Shanghai 200125, China)ABSTRACT: The work aims to apply the deep learning to the digital watermarking and propose a digital watermarking algorithm combining spatial domain and frequency domain based on multi-scale expanded convolutional attention (SF-ACA), so as to improve the invisibility and robustness of images while concealing information. The network framework of this algorithm consisted of two parts: a generator composed of ACA and SFE and a decoder. Among them, the MCA module in the ACA network combined three dilation convolutions with varying atrous rates for feature extraction of carrier images with multi-scale fusion, so that the carrier images could conceal the watermark information more effectively. The SFE combined fast Fourier convolution blocks to capture complementary information in the spatial and frequency domains with varied widths of perceptual fields to collect the feature information of the watermark more effectively and enhance the invisibility of the secret information and robustness. According to experimental findings, the PSNR value of the proposed watermarking method was 38.81 dB which was improved by 7.78% in comparison to the UDH method while concealing a color image of equal size to the carrier image. The watermarked image had a hiding capacity of 4 096 bits, and the method improved the extraction accuracy under Dropout, Gaussian noise, and JPEG attacks by 5.38%, 10.5%, and 1.65%, respectively, meeting the requirement of invisibility and achieving strong收稿日期：2023-04-26基金项目：上海市科学技术委员会科研计划（180****2500）；上海市自然科学基金面上项目（19ZR1435900）·194·包装工程2024年2月robustness. When the hiding capacity is high, the method described in this study performs better in terms of robustness and invisibility.KEY WORDS: deep learning; watermarking; attention mechanism; expanded convolution; Fourier transformation随着大数据时代的来临，数字通信和多媒体数据日益普及，数字水印在媒体通信安全、解决数字作品的版权纠纷[1]和识别数字作品的真伪方面[2]发挥了巨大作用。

A c a d e 信息安全与通信保密・２００６．８125徐　挺１ 杨　林２ 马琳茹３ 李京鹏２（１解放军理工大学通信工程学院研究生１队，南京　２１０００７２中国电子设备系统工程公司研究所，北京　１０００３９３国防科技大学电子科学与工程学院，长沙　４１００７３）【摘 要】论文借鉴扩频通信中的跳频技术，通过对数据收发端口号的不断跳变，实现网络信息的隐藏，探讨了跳端口技术在实际应用中从建立连接到数据通信过程中所涉及的技术原理、具体研究了密钥交换、数据同步、跳端口时间选择等几个关键技术，并进行了实验验证。

【关键词】通信 信息 跳端口Research of Port Hopping Technology Apply to Hiding CommunicationXu Ting 1 Yang Lin 2 Ma Lingru 3 Li Jingpeng 2(1 Postgraduate Team 1 ICE, PLAUST, Nanjing 210007 2Institute of China Electronic System Engineering, Beijing 1000393School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073)【Abstract 】This article uses the theories of the Technology of Frequency-Hopping in Spread-Spectrum communication. W ith changing the port number ceaselessly, it realizes the information hiding in internet. W e discuss Technology Theories from set up connection to information transmission with the Technology of Port Hopping. W e investigate some key technologies such as Key Exchange, Information Synchronization£¬Port Hopping Frequency and put up a experiment to validate.【Keywords 】 communication information port hopping跳端口技术在隐蔽通信中的应用研究１　前言随着信息网络的发展，信息技术不断深入日常生活的各方面，数据通信的安全性被人们所日益重视。

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I. J. Computer Network and Information Security, 2013, 12, 45-52Published Online October 2013 in MECS (/)DOI: 10.5815/ijcnis.2013.12.06Study and Analysis of Text Steganography ToolsIndradip BanerjeeDepartment of Computer Science & Engineering, National Institute of Technology,Durgapur, West Bengal, India.ibanerjee2001@Souvik BhattacharyyaDepartment Computer Science & Engineering,University Institute of Technology,Burdwan University, Burdwan, Indiasouvikbha@Gautam SanyalDepartment of Computer Science & Engineering, National Institute of Technology,Durgapur, West Bengal, India.nitgsanyal@Abstract — “Maintain the security of the secret information”, this words has been a great challenge inour day to day life. Sender can send messages regularly through a communication channel like Internet, drawsthe attention of third parties, hackers and crackers, perhaps causing attempts to break and expose the unusual messages. Steganography is a gifted region which is used for secured data transmission over any public media. Wide quantity of research work has been established by different researchers on steganography. Steganalysis is an art and science of detecting messages hidden using steganography. Some research work has also been remarked in the field of Steganalysis also. In this contribution, we have gone through steganalysis attack of some established text steganography tools.Index Term— Text Steganography, Text Steganalysis, Security, Cover Text, Stego TextI.I NTRODUC TIONInformation hiding is the ability to prevent or hidden certain aspects from being accessible to others excluding authentic user. It has many sub disciplines. One of the most important sub disciplines is steganography [1] which is derived from a work by Johannes Trithemus (1462-1516) entitled "Steganographia" and comes from the Greek language defined as "covered writing" [2]. It is an ancient art of hiding information in ways a message i s hidden in an innocent-looking cover media so that will not arouse an eavesdropper’s suspicion. Steganography diverges from cryptography in the sense that where cryptography focuses on keeping the contents of a message secret by encryption technique, steganography focuses on keeping the presence of a message secret [3], [4].Another form of information hiding is digital watermarking, which is the process that embeds data called a watermark, tag or label into a multimedia object such that watermark can be detected or extracted later to make an assertion about the object. The object may be an image, audio, video or text only [5], [6]. A hidden channel could be defined as a communications channel that transfers some kind of information using a method originally not intended to transfer this kind of information. Observers are unaware that a covert message is being communicated. Only the sender and recipient of the message notice it. Steganography works have been carried out on different media like images, video clips, text, music and sound [7], [4].In Image Steganography method the secret message is embedded into an image as noi se to it, which is nearly impossible to differentiate by human eyes [8], [9], [10]. In video steganography, same method may be used to embed a message [11], [12]. Audio steganography embeds the message into a cover audio file as noise at a frequency out of human hearing range [10]. One major category, perhaps the most difficult kind of steganography is text teganography or linguistic steganography because due to the lack of redundant information in a text compared to an image or audio. The text steganography is a method of using written natural language to conceal a secret message as defined by Chapman et al. [13].Figure 1: Types of Steganography46Study and Analysis of Text Steganography ToolsTEXT STEGANOGRAPHYThe affluence of electronic documented information available in the world as well as the exertion of serious linguistic analysis makes this an interesting medium for steganographic information hiding. Moreover the Text is one of the ancient media used in steganography. Letters, books and telegrams hide secret messages within their texts in earlier time i.e. before the electronic age comes. Text steganography refers to the hiding of information within text i.e. character-based messages. There are three basic categories of text steganography (Fig. 1) maintained here: format-based methods, random and statistical generation and linguistic methods.[14]i. Format-based methods [14]:This methods use the physical formatting of text as a space in which to hide information. Format-based methods usually modify existing text for hiding the steganographic text. Insertion of spaces or non-displayed characters, careful errors tinny throughout the text and resizing of fonts are some of the many format-based methods used in text steganography.ii. Random and statistical generation method [14]: This avoid comparison with a known plaintext, steganographers often resort to generating their own cover texts. Character sequences method hide the information within character sequences.iii. Linguistic methods [14]:The affluence of electronic documented information available in the world as well as the exertion of serious linguistic analysis makes this an interesting medium for steganographic information hiding.In case of text steganography, firstly, a secret message will be covered up in a cover-text by applying an embedding algorithm to produce a stego-text. The stego-text will then be transmitted by a communication channel to a receiver.TEXT STEGANALYS ISThe usage of text media, as a cover channel for secret communication, has drawn more attention [15]. This attention in turn creates increasing concerns on text steganalysis. At present, it is harder to find secret messages in texts compared with other types of multimedia files, such as image, video and audio [16-21]. In general, text steganalysis exploits the fact that embedding information usually changes some statistical properties of stego texts; therefore it is vital to perceive the modifications of stego texts. Previous work on text steganalysis could be roughly classified into three categories: format- based [22, 23], invisible character-based [24-26] and linguistics, respectively. Different from the former two categories, linguistic steganalysi s attempts to detect covert messages in natural language texts. In the case of linguistic steganography, lexical, syntactic, or semantic properties of texts are manipulated to conceal information while their meanings are preserved as much as possible[27].Due to the diversity of syntax and the polysemia of semantics in natural language, it is difficult to observe the alterations in stego texts. So far, many linguistic steganalysis methods have been proposed. In these methods, special features are designed to extend semantic or syntactical changes of stego texts. For example, Z.L. Chen [28] et al. designed the N-window mutual information matrix as the detection feature to detect semantic steganagraphy algorithms. Furthermore, they used the word entropy and the change of the word location as the semantic features [29, 30], which improved the detection rates of their methods. Similarly, C.M. Taskiran et al [31] used the probabilistic context-free grammar to design the special features in order to attack on syntax steganography algorithms. In the work mentioned above, designed features strongly affect the final performances and they can merely reveal local properties of texts. Consequently, when the size of a text is large enough, differences between Natural texts (NTs) and Stego texts (STs) are evident, thus the detection performances of the mentioned methods are acceptable. Whereas, when the sizes of texts become small, the detection rates decrease dramatically and cannot be satisfied for applications. In addition, some steganographic tool s have been improved in the aspectsof semantic and syntax for better camouflage [32]. Therefore, linguistic steganalysi s still needs further research to resolve these problems. Some more work on Text Steganalysi s has been discussed below.A. Linguistic Steganalysis Based on Meta Features and Immune Mechanism [33]: Linguistic steganalysi s depends on efficient detection features due to the diversity of syntax and the polysemia of semantics in natural language processing. This paper presents a novel linguistics steganalysis approach based on Meta features and immune clone mechanism. Firstly, Meta features are used to represent texts. Then immune clone mechanism is exploited to select appropriate features so as to constitute effective detectors. Our approach employed Meta features as detection features, which is an opposite view from the previous literatures. Moreover, the immune training process consi sts of two phases which can identify respectively two kinds of stego texts. The constituted detectors have the capableof blind steganalysis to a certain extent. Experiments show that the proposed approach gets better performance than typical existing methods, especially in detecting short texts. When sizes of texts are confined to 3kB, detection accuracies have exceeded 95.B. Research on Steganalysis for Text Steganography Based on Font Format [34]: In the research area of text steganography, algorithms based on font format have advantages of great capacity, good imperceptibility and wide application range. However, little work on steganalysis for such algorithms has been reported in the literature. Based on the fact that the statistic features of font format will be changed after using font-format-based steganographic algorithms, we present a novel Support Vector Machine-based steganalysis algorithm to detect whether hidden information exists or not. This algorithm can not only effectively detect the existence of hidden information,but also estimate the hidden information length according to variations of font attribute value. As shown by experimental results, the detection accuracy of our algorithm reaches as high as 99.3 percent when the hidden information length is at least 16 bits.The dimensionality of data from text file is normally huge; it is unrealistic to use the data directly for steganalysis. A feasible approach is to extract certain amount of data from the text and use them to represent the text itself for steganalysis. The features for steganalysis should reflect minor distortions associated with data hiding.Moments based FeatureTo construct the features of both cover and stego or suspicious text several moments of the series has been computed. In mathematics, a moment is, loosely speaking, a quantitative measure of the shape of a set of points. The "second moment", for example, is widely used and measures the "width" of a set of points in one dimension or in higher dimensions measures the shape of a cloud of points as it could be fit by an ellipsoid. Other moments describe other aspects of a distribution such as how the distribution is skewed from its mean, or peaked. There are two ways of viewing moments [35], one based on statistics and one based on arbitrary functions such as f (x ) or f (x , y ).Statistical view: Moments are the statistical expectation of certain power functions of a random variable. The most common moment is the mean which is just the expected value of a random variable as given in 1.∫∞∞−==dx x xf X E )(][µ(1)where f (x ) is the probability density function of continuous random variable X . More generally, moments of order p = 0, 1, 2, … can be calculated as m p = E[X p ].These are sometimes referred to as the raw moments. There are other kinds of moments that are often useful. One of these is the central moments])[(p p X E µµ−=. The best knowncentral moment is the second, which is known as the variance, given in 2.21222)()(µµσ−=−=∫m dx x f x(2)Two less common statistical measures, skewness and kurtosis, are based on the third and fourth central moments. Moments are easily extended to two or more dimensions as shown in 3.∫∫==dxdy y x f y x Y X E m qp q p pq ),(][ (3)Here f (x , y ) is the joint pdf.Estimation: However, moments are easy to estimate from a set of measurements, x i . The p -th moment is estimated as given in 4 and 5.∑−=Ni p ip xNm 11 (4)(Often 1/N is left out of the definition) and the p -th central moment is estimated as∑−=ip ip x xN)(1µ(5)x is the average of the measurements, which is theusual estimate of the mean. The second central moment gives the variance of a set of data 22µ=s . For multidimensional distributions, the first and second order moments give estimates of the mean vector and covariance matrix. The order of moments in two dimensions is given by p +q , so for moments above 0, there is more than one of a given order. For example, m 20, m 11, and m 02are the three moments of order 2.Non-statistical view: This view is not based on probability and expected values, but most of the same ideas still hold. For any arbitrary function f (x ), one may compute moments using the equation 6 or for a 2-D function using 7.∫∞∞−=dx x f xm pp )((6)∫∫=dxdy y x f y x m q p pq ),((7)Notice now that to find the mean value of f (x ), one must use m 1/m 0, since f (x ) is not normalized to area 1 like the pdf. Likewise, for higher order moments it is common to normalize these moments by dividing by m 0(or m 00). This allows one to compute moments which depend only on the shape and not the magnitude of f (x ). The result of normalizing moments gives measures which contain information about the shape or distribution (not probability dist.) of f (x ).Digital approximation: For digitized data, we must replace the integral with a summation over the domain covered by the data. The 2-D approximation is written in 8.∑∑∑∑−−−−==M i Nj q p M i N j q jp ij i pq j i j i f y x y x f m 1111),(),((8)If f (x , y ) is a binary matrix function of an object, the area is m 00, the x and y centroids are 9 and 10.0010/m m x =(9)0001/m m y = (10)To implement the attack of text Steganography we use some stego text from some available steganography tools which are discussed below.SNOW DOS 32 [37]: The encoding system used by snow depend on the fact that spaces and tabs (known as whitespace ), when appearing at the end of lines, are invisible when presented in pretty well all text viewing programs. This allows messages to be hidden in ASCII text without affecting the text's visual representation. And since trailing spaces and tabs occasionally occur naturally, their existence should not be sufficient to immediately alert an observer who stumbles across them.wbS tego4.3open [36]: This module of steganography has been published under the GNU General Public License (GPL). wbStego uses a custom mechanism for localization. All information is stored in the data file, which consists of a number of blocks, all introduced by a 3 byte header specifying the size of the block. The file is a terminated by 3 bytes set to 0, i.e. a block header without data block.In this paper, we have analyzed the text steganalysi s by the help of statistical moment’s technique using two of steganography tools. After wide research on steganography by author’s previous work [11], [12], [13], [14], [15], [16], [17], [18], [33], [34], author is going to start work on the analysis part of text steganography.This paper is organized into the following sections. Section II describes the proposed model. Analyses of the results are in section III. The last section descries the concluded part of the work.II. P ROPOSED M ODELText steganalysis, at all this paper exactly deals with, uses two steganography tools (SNOW DOS 32 and wbStego4.3open). We have selected a cover and then create stego text by inserting various length of secret message. After that find out the moments up to 10 orders and observed that wbStego4.3open is better than SNOW DOS 32 at the side of embedding capacity.Figure 4: 2nd Order Moment values of SNOW & wbStego4.3open.Here we have observed that the value of moment in various length has changes. Simultaniously it also occurs in between 2nd order moment to 10th order moment which is furnished in Figure 2 and Figure 3 of SNOW DOS 32 and wbStego4.3 open steganography softwares.A. Solution MethodologyThe proposed system involves two software windows i.e. SNOW DOS 32 and wbStego4.3open. The user will be someone who is aware with the process of information hiding and will have adequate knowledge of steganography systems. The user first selects the plain text message from a file or enters text in specified area of software, another text to be used as the carrier (cover text). Then every tool will hide the message in the selected cover text and will procedure the stego text. Then create stego in various length of message and find out the moments which are shown in Fig. 2 and 3. After the comparison in between these values it has been observed that the system wbStego4.3open has minimum changes found for each order of moment (Fig. 4, Fig. 5), whereas the SNOW DOS 32 graph is decreasing for increase of embedding capacity.III.E XPERIMENTAL R ESULTSThe steganalyzer has been designed based on a training set and using various text steganographic tools. The steganographic tools used here SNOW DOS 32 & wbStego4.3open. In the experiments one cover and 30 input message were used for training and 20 cover text for testing. These experiments are performed using a large data set of text document obtained from publicly available websites. The data set i s categorized with respect to different features of the text to determine their potential impact on steganalysis performance. Fig.6 and Fig.7 shows that the graphical representation of 2nd Order Moment of SNOW DOS 32 and wbStego4.3open. Here it has been observe that the graph SNOW is decreasing for high embedding where as the wbStego graph is increasing for high embedding. So it has proved that the performance and capasity of wbStego is better than SNOW.IV.C ONCLUSIONSIn this paper, text based steganalysi s techniques of some module is tested based on moments and other similarity measure feature to evaluate what is the best. The plane text has been selected as an estimate of the cover-object. Next step is to use stati stical, invariant and other similarity measure features to measure the distortion and to determine the presence of hidden information in a text. Results from moments with numerous text series showed that the proposed steganalysis algorithm provides significantly better analysi s rates than existing ones. The author’s future goal is to compare these tool s with author’s own generic module of steganography.Figure 5: 7th Order Moment values of SNOW &wbStego4.3open.Figure 6: 2nd Order Moment graph of wbStego4.3open.Figure 7: 2nd Order Moment graph of SNOW.R EFERENCES[1]Fabien A.P. Petitcolas, Ross J. Anderson, MarkusG. Kuhn: Information Hiding—A Survey,Proceedings of the IEEE, Vol. 87, No. 7, July1999, pp. 1062-1078, ISSN 0018-9219.[2]K. Bennett. Linguistic steganography: Survey,analysis, and robustness concerns for hiding information in text. Purdue University, CERIASTech. Report, 2004.[3]Ross J. Anderson. and Fabien A.P.Petitcolas. 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Dittmann, “Pros and cons ofmelcepstrum based audio steganalysis using SVMclassification”, The 9th International Workshop onInformation Hiding, Saint Malo, France, pp.359–377, 2007.[17]M.E. Choubassi, P. Moulin, “Noniterativealgorithms for sensitivity analysis attacks”, IEEETransactions on Information Forensics and Security, Vol.2, No.3, pp.113–126, 2007.[18]O.H. Kocal, E. Avcibas, “Chaotic-type features forspeech steganalysis”, IEEE Transactions on Information Forensics and Security, Vol.3, No.4,pp.651–661, 2008.[19]Z.J. Wu, Y. Hu, X.X. Niu, H.X. Duan, X. Li,“Information hiding technique based speech securecommunication over PSTN”, Chinese Journal ofElectronics, Vol.15, No.1, pp.108–112, 2009. [20]H. Shan, K. Darko, “An estimation attack oncontent-based video fingerprinting”, Transactionson Data Hiding and Multimedia Security II,Vol.4499, No.2007, pp.35–47, 2007.[21]R. 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Bennett, “Linguistic steganography: Survey,analysis, and robustness concerns for hidinginformation in text”, Purdue University, Indiana,USA, 2004.[33]YA NG Hao and CAO Xianbin “ LinguisticSteganalysis Based on Meta Features and ImmuneMechanism “Chinese Journal of Electronics,Vol.19, No.4, Oct. 2010[34]Lingyun Xiang, Xingming Sun, Gang Luo, CanGan. “Research on Steganalysis for TextSteganography Based on Font Format”, The ThirdInternational Symposium on InformationAssurance and Security (IAS 2007), Manchester, United Kingdom , August 2007. [35] MOMENTS IN IMA GE PROCESSING Bob Bailey Nov. 2002 [36] Available online: http://home.tele2.at/wbailer/wbstego/wbs4devdoc .html [37] Available online: .au/snow/ [38] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. “Novel text steganography through special code generation.” In Proceedings of International Conference on Systemics,Cybernetics and Informatics (ICSCI-2011), Hyderabad,India., Jan 5-8, 2011. [39] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. “The text steganography using article mapping technique(AMT) and SSCE”. Journal of Global Research in Computer Science, 2, April 2011. [40] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. Design and implementation of a secure text based steganography model. In 9th annual Conference on Security and Management (SAM) under The 2010 World Congress in Computer Science,Computer Engineering and Applied Computing(WorldComp 2010), Las Vegas,USA, July 12-15,2010. [41] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. Implementation of a novel text based steganography model. In National Conference on Computing and Systems (NACCS), Dept. of Computer Science, The University of Burdwan, Burdwan,India., Jan 29, 2010. [42] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. A novel approach of secure text based steganography model using word mapping method(WMM). International Journal of Computer and Information Engineering 4:2 2010 - World Academy of Science, Engineering and Technology (WASET), 4:96103, Spring 2010. [43] Souvik Bhattacharyya, Indradip Banerjee, Arka Prokash Mazumdar and Gautam Sanyal. Text steganography using formatting character spacing. IJICS, 13, Decembar, 2010. [44] Souvik Bhattacharyya, Indradip Banerjee and Gautam Sanyal. A survey of steganography and steganalysi s technique in image, text, audio and video as cover carrier. Journal of Global Research in Computer Science, 2, April 2011. [45] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. An Approach of Quantum Steganography through Special SSCE Code. International Journal of Computer and Information Engineering - World Academy of Science, Engineering and Technology (WASET), Issue 0080:2011, Article 175, Page: 939-946. [46] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. Text Steganography through Quantum Approach. In Journal on Wireless Networks And Computational Intelligence, Communications in Computer and Information Science, 2012, Volume 292, Part 7, 632-643, DOI:10.1007/978-3-642-31686-9_74 Springer-Verlag Berlin Heidelberg 2012.[47] Indradip Banerjee, Souvik Bhattacharyya and Gautam Sanyal. "A Procedure of Text Steganography Using Indian Regional Language"Journal on "I. J. Computer Network and Information Security, 2012, v. 8, p. 65-73"Published Online August 2012 in MECS.Indradip Banerjee is a Research Scholar at National Institute ofTechnology, Durgapur, West Bengal, India. He received his MCA degree from IGNOU in 2009, PGDCA from IGNOU in 2008, MMM from Annamalai University in 2005 andBCA (Hons.) from The University of Burdwan in 2003. He is pursuing his PhD. in Engineering at Computer Science and Engineering Department, National Institute of Technology, Durgapur, West Bengal, India. His areas of interest are Steganography, Cryptography, Text Steganography, Image Steganography, Quantum Steganography and Steganalysis. He has published 16 research papers inInternational and National Journal s / Conferences. Souvik Bhattacharyya received his B.E. degree in Computer Science and Technology from B.E. College, Shibpur, India and M.Techdegree in Computer Science and Engineering from National Institute of Technology, Durgapur, India. Currently he is working as an Assistant Professor and In-Charge in Computer Science and Engineering Department at University Institute of Technology, The University of Burdwan. His areas ofinterest are Natural Language Processing, Network Security and Image Processing. He has published nearly 65 papers in International and National Journals / Conferences.Gautam Sanyal has received his B.E and M.Tech degree National Institute of Technology (NIT), Durgapur, India. He has receivedPh.D (Engg.) from Jadavpur University, Kolkata, India, in the area of Robot Vision. He possesses an experience of more than 25 years in the field of teaching and research. He has published nearly 150 papers in International and National Journals / Conferences. Two Ph.Ds (Engg) have already beenawarded under his guidance. At present he is guidingsix Ph.Ds scholars in the field of Steganography,。

数字图像水印算法和研究数字图像水印算法和研究摘要随着计算机通信技术的迅速发展，多媒体存储和传输技术的进步使存储和传输数字化信息成为可能，然而，这也使盗版者能以低廉的成本复制及传播未经授权的数字产品内容。

密码学的加解密技术是保护数字产品的一种方法，它能够保护数字产品安全传输，并可作为存取控制和征收费用的手段，但它不能保证数字产品解密后的盗版问题，因此，1995年，人们提出了信息伪装技术，其中，数字水印就是近年来比较热门的数字产权保护技术。

数字水印技术属于信息安全的范畴，是信息隐藏的一个分支。

数字图像水印算法是数字水印技术研究的一个方面，具有极大的理论研究价值和应用前景，是目前学术研究的一个热点。

该文论述了数字水印的提出及研究现状；水印的基本原理和算法;最后给分析了展望关键词数字水印算法Digital Image Watermarking Algorithm and researchAbstractWith the rapid development of communication technology, multimedia storage and transmission technology to enable storage and transmission of digital information possible, however, that the pirates can make low cost of unauthorized copying and dissemination of the contents of digital products. Cryptography is the encryption and decryption technology to protect digital products in a way that it can protect the secure transmission of digital products, and Access control and as a means of charging, but it can not guarantee that the post-decryption digital piracy problem, therefore, 1995 years, the information raised camouflage technology, includingdigital watermarking is relatively popular in recent years the numberof property rights protection.Digital watermarking technology is the scope of information security is a branch of information hiding. Digital image watermarking algorithm is a study of digital watermarking technology, one Aspect of the theory of great research value and application prospect, is a hot academic research. The article discusses the proposed digital watermarking and examine the status quo; the basic principle of watermarking and algorithm; the final analysis of the outlook..Keywords Digital WatermarkingAlgorithm目录摘要 IAbstract II第1章绪论 11.1 背景 11.2 研究现状 1第2章基本原理和算法 32.1 水印技术 32.2 水印算法分类 42.2.1 空间域水印 42.2.2 变换域算法 42.2.3 压缩域算法 52.2.4 NEC算法 62.2.5 生理模型算法 6第3章水印的分类和攻击 73.1 水印的分类 73.2 水印的攻击 8第4章小波变换的灰度水印嵌入算法 104.1 水印的嵌入算法 104.2 对灰度水印图像进行二值序列化 104.3 水印嵌入的步骤 114.3.1 水印嵌入系数的产生 114.3.2 邻域平均值的获得方法 114.3.3 水印嵌入的具体步骤 114.4 水印的提取及分析 124.4.1 水印数据的提取 124.4.2 嵌入水印后图像与原图像的灰度值对比 13 4.5 抗攻击性的测试 134.5.1 剪切攻击 134.5.2 图像灰度值调整攻击 144.5.3 JEPG压缩攻击 14第5章水印的主要应用领域 15第6章结论 16致谢 17参考文献 18第1章绪论1.1 背景近年来，数字化技术和Internet的飞速发展，在最大限度地拓宽权利人利益范围的同时，也带来了版权保护的危机。

简要说明信息隐藏及提取流程## Information Hiding and Extraction Process.Information Hiding:Information hiding is the process of concealing information within a digital carrier such as an image, audio file, or video. The goal is to embed the hidden data in a way that makes it difficult for unauthorized individuals to detect or extract it. This technique is widely used for various purposes, including copyright protection, data security, and covert communication.Process of Information Hiding:1. Data Preparation: First, the secret data (e.g., a message, image, or file) is prepared for embedding. This may involve compression, encryption, or other transformations to minimize its size and increase its robustness.2. Carrier Selection: A suitable digital carrier is chosen based on factors such as its size, complexity, and ability to conceal the hidden data. Common carriers include images, audio files, and videos.3. Embedding Algorithm: An embedding algorithm is employed to encode the prepared data into the carrier. This process involves modifying the carrier's characteristics subtly while maintaining its overall integrity.4. Watermarking: A watermark can be added to the modified carrier to indicate the presence of hidden data and protect it against unauthorized modifications.Information Extraction:Information extraction is the process of recovering the hidden data from the carrier. Only authorized individuals with the knowledge of the embedding method and the proper extraction tools can successfully retrieve the embedded information.Process of Information Extraction:1. Carrier Analysis: The modified carrier is analyzed to identify the presence of hidden data. This can be done using watermark detection algorithms or other techniques.2. Algorithm Selection: The appropriate extraction algorithm is chosen based on the embedding method used.3. Decoding: The hidden data is decoded and reconstructed using the specific algorithm.Applications of Information Hiding and Extraction:Copyright Protection: Watermarking images, videos, and other digital content helps deter unauthorized copying and distribution.Data Security: Confidential data can be embedded in harmless-looking files for secure storage and transmission.Covert Communication: Sensitive information can be conveyed through seemingly innocuous messages or images, making it difficult for eavesdroppers to detect.Medical Imaging: Digital patient records can be watermarked with patient-specific information, enhancing security and traceability.Advantages of Information Hiding:Increased Security: Hidden data is less susceptible to unauthorized access and modification.Enhanced Privacy: Sensitive information can be concealed within everyday objects, making it difficult to track and identify.Improved Data Integrity: Watermarking can help detect and prevent unauthorized alterations to digital content.Limitations of Information Hiding:Capacity: The amount of data that can be hidden is limited by the carrier's capacity and the embedding method used.Detectability: Some embedding methods may leave detectable traces in the carrier, making the hidden data vulnerable to discovery.Retrieval Difficulty: Extracting hidden data can be challenging, especially if the extraction algorithm or key is not available.## 信息隐藏和提取流程。

多媒体信息隐藏技术张新鹏;殷赵霞【摘要】信息安全研究在攻击与防守的对抗中不断发展.一方要攻,要制造尖锐的矛;另一方要守,要制造坚固的盾.多媒体信息隐藏是信息安全与数字信号处理两个领域的交叉.本文回顾了多媒体信息隐藏研究的历史,根据应用领域和技术目标的不同,从数字水印、隐蔽通信和密文域信息隐藏三个方面讲述了多媒体信息隐藏技术如何在攻与防的对抗中互相促进与发展.%As we well know,attack and defense are two basic sides of information security.One aims at sharp spear and the other focuses on strong shield.During the confrontation of the two sides,the technology gains breakthrough time after time.Data hiding in multimedia is a domain intercrossed between information security and signal processing.This thesis firstly reviews the development history of multimedia data hiding studies and elaborateswatermarking,steganography and cipher domain data hiding respectively,according to different applications and technical indicators.It is shown that attack and defense have always been mutually promoting and restricting in the development of multimedia data hiding studies.【期刊名称】《自然杂志》【年(卷),期】2017(039)002【总页数】9页(P87-95)【关键词】信息隐藏;数字水印;隐蔽通信;密文域信息隐藏【作者】张新鹏;殷赵霞【作者单位】上海大学通信与信息工程学院,上海200444;上海大学通信与信息工程学院,上海200444;安徽大学计算机科学与技术学院,合肥230601【正文语种】中文1948年是不寻常的一年，香农(C. E. Shannon)发表了重要文章《通信的数学理论》，标志着信息论的创始；同样在1948年，诺伯特•维纳 (Norbert Wiener)发表了《控制论——关于在动物和机器中控制和通信的科学》，标志着控制论的诞生。

一种基于伪随机的支持大尺寸图像水印算法王树梅;张文斌【期刊名称】《计算机技术与发展》【年(卷),期】2017(027)008【摘要】数字水印技术是信息隐藏的一种重要技术,在保护数字图像版权方面发挥着重要作用.提出了一种支持大尺寸的数字图像水印算法.该算法将大尺寸的图像作为水印信息嵌入到被保护的对象里,亦即将大尺寸的水印图像信息依据载体图像的特征大小获取水印信息量,并基于伪随机编码器确定水印信息的采样和嵌入位置,在对嵌入信息置乱的基础上对载体信息进行离散余弦变换,应用自适应方式将置乱后的水印信息嵌入到变换域的低频位置.自适应嵌入方式中的嵌入因子由嵌入位置的水印信息大小和载体像素值大小确定,需要对原始图像和含水印图像进行处理,在确定水印所在位置后,利用嵌入的逆运算提取出水印信息,利用逆置乱算法还原水印信息到原始水印图像.验证实验结果表明,所提出的算法具备较强的实用性和安全性.%Digital watermarking is an important technology of information hiding and plays an important role in protection of copyright of digital images.A digital watermarking algorithm with support of large size isproposed,which embeds images of large size as watermark information in the protected object.That means the watermark information amount is acquired by watermark image information with large size according to the feature of the carrier image,and the sampling and embedded location of watermark information is determined based on the pseudo-random encoder.Then the carrier information is carried on the discrete cosinetransform on the basis of embedded information scrambled,using the adaptive way to embed the scrambled watermark information into low-frequency location of the transformed domain.The embedding factor in the adaptive embedding method is determined by the watermark information size and the vector pixel value of the embedding position.The original image and the watermarked image need to be processed.After positioning the watermark,the embedded inversion is used to extract the watermark information and the watermark information is restored to the original watermarked image by the inverse scrambling algorithm.The experimental results show that it has strong practicability and security.【总页数】5页(P121-124,129)【作者】王树梅;张文斌【作者单位】江苏师范大学计算机学院,江苏徐州 221011;江苏师范大学计算机学院,江苏徐州 221011【正文语种】中文【中图分类】TP391【相关文献】1.基于小波和伪随机变换的盲检测图像水印算法 [J], 陈森;张兴周2.基于SVD和伪随机循环链的图像认证水印算法 [J], 宋伟;侯建军;李赵红3.一种新的基于伪Zernike矩的图像盲水印算法 [J], 陈青;翁旭峰4.一种基于特征点和伪Zernike矩的数字图像水印算法 [J], 朱世元;王凤英5.基于伪随机的图像水印算法 [J], 唐永军因版权原因，仅展示原文概要，查看原文内容请购买。

一种抵抗JPEG压缩的数字水印方案付艳辉;郑文超;李黎【摘要】数字水印是对数字媒体产品进行产权保护的一种重要方法.该文提出一种抵抗联合图像专家小组压缩攻击的水印算法,对载体图像进行部分解码,将水印以奇偶原则嵌入到量化后的直流分量系数中,再编码成图像.算法简单易实现,对原图修改较少,有良好的透明性,并能抵抗常见图像处理软件的压缩攻击.【期刊名称】《杭州电子科技大学学报》【年(卷),期】2012(032)006【总页数】4页(P105-108)【关键词】数字水印;压缩;量化表【作者】付艳辉;郑文超;李黎【作者单位】杭州电子科技大学计算机学院,浙江杭州310018;杭州电子科技大学计算机学院,浙江杭州310018;杭州电子科技大学计算机学院,浙江杭州310018【正文语种】中文【中图分类】TP3910 引言数字水印技术是信息隐藏的一个分支。

如今信息技术的飞速发展，给人们的生活带来便利的同时也带来了信息安全问题。

数字水印是对数字媒体进行知识产权保护的一种重要且有效的手段。

自从数字水印概念被提出以来，水印技术已越来越多地受到人们的重视，并已成为多媒体信息安全领域研究的一个新的热点［1］。

数字水印是在数字媒体中嵌入水印信息以保护其版权或增强嵌入信息的隐蔽性。

数字水印从嵌入位置分类可以大致分为空域算法和变换域算法。

其中最优代表性的是最低有效位法，文献2、3提出，这是一种典型的空间域隐藏方法。

变换域算法有可以细分为离散余弦变换(Discrete Cosine Transformation，DCT)、小波变换、傅立叶变换等。

文献4提出对整个图像做DCT变换，然后将水印序列嵌入到频谱系数中。

文献5提出了一种在小波域实现的数字水印方法，其优点是稳健性好和层次性检测方法。

文献6提出的两个具有代表性的DFT域的水印算法，一个是将水印嵌入到DFT系数的相位谱中，以达到防篡改的目的，另一个是将水印嵌入到DFT系数的幅度谱中，以实现水印的平移、旋转和尺度拉伸不变性。

给Word与PDF文档添加水印什么叫水印所谓数字水印是向多媒体数据(如图像、声音、视频信号等)中添加某些数字信息以达到文件真伪鉴别、版权保护等功能。

嵌入的水印信息隐藏于宿主文件中，不影响原始文件的可观性和完整性。

数字水印过程就是向被保护的数字对象(如静止图像、视频、音频等)嵌入某些能证明版权归属或跟踪侵权行为的信息，可以是作者的序列号、公司标志、有意义的文本等等。

与水印相近或关系密切的概念有很多，从目前出现的文献中看，已经有诸如信息隐藏(Information Hiding )、信息伪装(Steganography )、数字水印(Digital Watermarking )和数字指纹(Fingerprinting )等概念。

WORDWord 2007本身提供添加水印的功能，位于“页面布局-水印”，自定义水印如下：在项目应用中的话，可以调用Office的VBA函数实现这一功能，代码如下:Ac ti we Do cum ent. Sect i ons (1.1. R^rLge . Sal eelActivetfiiiilo*. ActireTane. View- SeekVi = ^rdS^ eWL ur r entT ageMe ad er Selecti cn.K^adsTFooler. Slkapss. Ad dT fex IE€£*et CFow<rFlii5Vater M arWObjtc 110832484. ”诘刘措"宋待：i false, False, 0, D〕.SelectSelecti cn. STiapefLange. Najne = o w errinsWater H arkC b j ec110832434A'Select i^n. S'hapeEanf e. Te^tEff^ct ・Ikinnali 卫會dhki^ht 二F als&Selec ti<m, SMpeJtamge. L IM S Visible 二Fal瓦电Selec七:(昭n. £haj>ER：aii耳巴FiI2L. wibl启— TrueSelection. SliapeEaiige. FiU. SolidSelection.. SKape^ange. Fill. ForeColor. RGB 二RG-B; (192^. 1 92^Sel«ction_ Shape^anga. FiU. Transparency - 0. 5Selection. SliapeEaiige. Rot^ati on - 315Selection. Shspelonge. LodcA^p^clRati o 二TruaSelect i ert. SltfipeEang*. Hsi t ght = C«nti metfer sToFoints (4 13)Selection. ShapeEange. Width - C^ntimetersToFoirLts (IB. 523S«l«£ti4n. ShapeKangt. Vr^pFormat. AllowO^txlap - TruaaSslttctivn. ShapeKenia. WraciFormat.Side - w d!Yr«.pIaaLSSelec ti叩rv ShapeRai备◎ WirgpFojTn：at・Typ2 = 3Sel^e ti专rv SBsipEF込百邑.Re 1 i riz*ntaLPasiti*n 二 _iveVerti caLFowi ti弔nfdo~倉；inSelect I CTL Sliape^ariga. Rel^.ti v^Ver ticalPtbsi ti^n.二 .wdK^la.t 亡VEfli c^U QSL ti^nf^urginShapelaiig^. Lsft - wd£hap&CenterSel^cti^n. Shape£a^ge. Top =Actiw liniw Acti^Pan包.Vijsw.StskVi = wd£#M lirlift 亡顷色nt图2若是使用NTKO空件调用，则需要先获得ActiveDocument , NTKOI供获取该对象的方法，其说明如下：1* {&ctiveDocument^AcnveDocument：遼回文档对獄的自功优第口.貝彳4 *'读取匸d^cObj. = Obj ActiveDocument-比如*如果打芥—? Word文档.则ActiveDocuent代丧一牛■;/ora Document ・可朝甲javascript或者VBScript时诜对舉进行控制来揃作0川“文档.例如‘利用箝向当前文档怖入文宇古㈠QB>LAcU¥ftDjKumnLApaiijGAiWLDJ^j8u«Muiyaeijmr 扩）：*图3PDF给PDF文档加入水印或加密，应用在项目中的话，需要使用第三方API进行实现, 这里可以选择itext。

基于矢量量化压缩编码的数字水印杨刚;都思丹【期刊名称】《现代电子技术》【年(卷),期】2014(000)001【摘要】A watermarking scheme based on the technique of VQ coding is proposed. After VQ coding to the original image, the code word is classified according to the level of similarity in codebook. A random serial generated on the basis of the watermark size is taken as the secret key,and then the watermark is embedded into special positions of the compressed data according to the key. The main feature of the proposed watermarking scheme is that the watermark exists both in VQ coded compressed data of original image and the VQ decoded image of receiving end. The compressed data is much less than the original data in data size,so the network transmission time and storage space can be saved by taking compressed data instead of the original image to carry the watermark. The scheme is very advisable for watermark embedding and detecting in network environment. Further more,the scheme has better robustness that can against image attacking,such as cropping,blurring,JPEG compressing and so on.%提出一种基于矢量量化压缩编码（简称VQ编码）技术的水印策略，在对原图像进行VQ编码后，按码书中码字的相似程度对码字进行划分，根据待嵌入水印图像的大小产生一个随机序列作为密钥，然后根据密钥在压缩数据的特定位置嵌入水印。