本文選題：可逆圖像水印 + 紋理分析　； 參考：《江西理工大學(xué)》2017年碩士論文

【摘要】：大家的學(xué)習(xí)和工作因為互聯(lián)網(wǎng)的飛快發(fā)展給帶來了極大的方便,同時也帶來諸如盜版、信息篡改等一系列潛在的信息安全問題。為了解決該問題,傳統(tǒng)的方法采用加密和數(shù)字簽名等技術(shù),但加密使得不法分子容易看出要傳輸?shù)耐ㄐ判畔?從而獲取和破解他們感興趣的信息。而數(shù)字簽名雖然能夠為數(shù)據(jù)的傳輸提供有效的保護,但需要在原始數(shù)據(jù)中加入大量簽名,同時隨著并行計算的發(fā)展,數(shù)字簽名的安全性已經(jīng)受到質(zhì)疑。為了解決傳統(tǒng)技術(shù)上的缺陷問題,數(shù)字水印技術(shù)被提出,利用載體冗余性來嵌入隱蔽信息,提高載體傳輸?shù)陌踩�。但是傳統(tǒng)的數(shù)字圖像水印在嵌入隱藏信息后,載體圖像無法還原到最初始狀態(tài),對圖像質(zhì)量要求極高的特殊范圍領(lǐng)域是難以接受的。為了解決該問題,學(xué)者們提出了可逆水印,可逆水印能夠無損恢復(fù)最初始圖像和完全提取隱藏信息,在醫(yī)學(xué)、工程、軍事等領(lǐng)域具有很好的發(fā)展前景。本文以灰度圖像為研究對象,通過圖像的紋理特征分析,提出基于紋理分析的無損水印算法,本文的主要工作如下:(1)針對預(yù)測器的預(yù)測準確度和隱藏信息圖像遮蔽性上不足的問題,提出利用領(lǐng)域八個方向的梯度預(yù)測和自適應(yīng)選擇水印嵌入塊的可逆圖像水印。該方法充分考慮到圖像周圍像素的相關(guān)性,利用均方誤差給鄰域八個像素分配權(quán)值,然后計算預(yù)測像素值。并采用均方誤差分析每個子塊紋理復(fù)雜度,選擇紋理最復(fù)雜的子塊進行水印嵌入,在嵌入?yún)^(qū)域采用基于預(yù)測誤差對方法嵌入水印。算法提高了水印的遮蔽性和預(yù)測精準度。(2)針對灰度共生矩陣的四個主要特征參數(shù)與圖像子塊紋理復(fù)雜度之間沒有明顯的數(shù)學(xué)關(guān)系和水印遮蔽性上不足的問題,提出一種基于灰度共生矩陣特征參數(shù)用于分析圖像紋理的可逆圖像水印。通過提取灰度共生矩陣四個特有的特征參數(shù),利用均方誤差給每個特征參數(shù)分配權(quán)重值,從而建立灰度共生矩陣的特征參數(shù)與載體圖像紋理復(fù)雜度的數(shù)學(xué)關(guān)系;然后把宿主圖像分塊,利用圖像復(fù)雜度的數(shù)學(xué)關(guān)系去計算子塊紋理復(fù)雜度,選擇復(fù)雜度最大的子塊去嵌入隱藏信息。該方法對于自然圖像和醫(yī)學(xué)圖像尤其是紋理復(fù)雜的圖像取得更好的嵌入效果。
[Abstract]:Because of the rapid development of the Internet, people's study and work bring great convenience, but also bring about a series of potential information security problems, such as piracy, information tampering and so on. In order to solve this problem, the traditional methods use encryption and digital signature techniques, but encryption makes it easy for illegal elements to see the communication information to be transmitted, so as to obtain and decipher the information they are interested in. Although digital signature can provide effective protection for data transmission, it needs to add a large number of signatures to the original data. With the development of parallel computing, the security of digital signature has been questioned. In order to solve the problem of traditional technology, digital watermarking technology is proposed, which uses carrier redundancy to embed hidden information to improve the security of carrier transmission. However, the traditional digital image watermarking can not be restored to the initial state after embedding hidden information, so it is difficult to accept the special field which requires high image quality. In order to solve this problem, researchers have proposed reversible watermarking, which can restore the initial image without damage and extract hidden information completely. It has a good prospect in medicine, engineering, military and other fields. In this paper, a lossless watermarking algorithm based on texture analysis is proposed by analyzing the texture features of gray-scale images. The main work of this paper is as follows: (1) aiming at the problem of poor prediction accuracy and hidden information image masking of the predictor, this paper proposes to use the gradient prediction of eight directions of the domain and the adaptive selection of reversible image watermarking in the watermark embedding block. In this method, the correlation of the pixels around the image is fully taken into account, and the mean square error is used to distribute the weights of the neighboring eight pixels, and then calculate the predicted pixel values. The texture complexity of each sub-block is analyzed by mean square error, and the most complex texture sub-block is selected to embed the watermark, and the prediction error pair method is used to embed the watermark in the embedded region. The algorithm improves watermark masking and prediction accuracy. (2) there is no obvious mathematical relation between the four main characteristic parameters of gray level co-occurrence matrix and texture complexity of image subblock, and the problem of watermark masking is insufficient. A reversible image watermarking based on gray level co-occurrence matrix feature parameters is proposed to analyze image texture. By extracting the four characteristic parameters of the gray level co-occurrence matrix and using the mean square error to assign the weight value to each characteristic parameter, the mathematical relationship between the feature parameters of the gray level co-occurrence matrix and the texture complexity of the carrier image is established. Then the host image is divided into blocks, and the texture complexity of the sub-block is calculated by using the mathematical relation of image complexity, and the most complex sub-block is selected to embed the hidden information. This method can achieve better embedding effect for natural and medical images, especially for images with complicated texture.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP309.7

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