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研究生:柯朝輝
論文名稱:頻域上基於人類視覺系統之適應性影像浮水印技術
論文名稱(外文):Adaptive Image Watermarking Based on Human Visual System in Frequency Domain
指導教授:蔡清欉蔡清欉引用關係
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:134
中文關鍵詞:數位浮水印人類視覺系統離散餘弦轉換恰可視誤差智慧財產權保護
外文關鍵詞:digital watermarkhuman visual systemDiscrete Cosine TransformJust-Noticeable-Distortioncopyright protection
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由於網際網路的普及,使用者可以透過網路輕易取得這些數位資料並加以修改,使得資料擁有者的著作權受到很大的威脅。為了保護網路多媒體的智慧財產權,一種能將版權宣告訊息藏入多媒體資料中的版權保護機制數位浮水印便應運而生。
目前的浮水印技術依其嵌入浮水印的方式來看,可以分為空間域與頻域嵌入浮水印兩大類。本文針對頻域嵌入技術來探討,頻域的嵌入技術雖然強健性(robustness)較高但是較難評估是否合乎隱藏性(imperceptibility)的要求。本文嘗試克服上述困難,所提出的作法是在空間域中模擬訊號處理所造成影像灰階值的改變,但為了確保經模擬攻擊後的影像不會產生可視的失真,我們利用人類視覺系統的特性估測的恰可視誤差(just noticeable distortion,JND)值作為影像中灰階值可以承受的最大修改量。如此將經模擬攻擊後的影像映至頻域藉以獲得頻域係數的變化量,並依此作為嵌入浮水印資訊量的最大強度。而選擇嵌入浮水印之頻域係數是依據頻率位置、係數值及加入資訊量的多寡三個因素。此外,我們考慮影像性質將影像分割成互不相重疊的影像區塊,然後根據區塊分類不同的特性來決定頻域係數嵌入浮水印時符合該區塊之最大隱藏的資訊量,藉以提高浮水印的強健性。多重浮水印技術(multi-watermarking technique)也是本論文探討的另一個重點,本文提出一個互補式更正(complementary correction)法則來禰補單一浮水印技術對於擷取性攻擊(cropping attack),受制於擷取資訊量的多寡的缺點。本論文根據訊號處理所造成頻域係數值變化的特性加以分析取出浮水印的正誤,然後以它組浮水印來更正,使得浮水印在遭受擷取性的攻擊時依然能從少量擷取資訊量中將浮水印取出。
總而言之,本論文將人類視覺系統的特性應用到頻域來,而且能確保嵌入的浮水印在空間域中是不可見的。此外,並提出位置強度模型與互補式更正法則使嵌入的浮水印具有高度的強健性可以抵抗人為的攻擊破壞。根據實驗的結果顯示,本文所提出的浮水印系統具有高度的強健性,它能抵抗JPEG壓縮、部份影像擷取、雜訊、模糊等各類訊號處理的攻擊。
Due to the prevalence of Internet, the various information is digitizing rapidly and can be accessed easily. People can reproduce and manipulate these digital data without granting appropriate credit to the owner. Therefore, how to protect data on Internet is one the important issue owners should face. One promising solution for the copyright protection of digital images is a so-called watermarking technique. The watermarking technique can hide an invisible signature or code in digital image to indicate the owner or recipient.
The current watermarking schemes can be classified into two categories: spatial domain approach and frequency domain approach. Although the frequency domain techniques are robust when various signal-processing attacks, it is difficult for them to evaluate visual imperceptibility. For the purpose of overcoming mention above, the method of simulating attack is adopted to simulate signal-processing operations that modify the grayscale value of the image in spatial domain. First, we use a just-noticeable distortion (JND) based on human visual model to check out the maximal intensity of simulating attack. The image operated by simulating attack is transformed to the frequency domain using discrete cosine transform (DCT) and then the change of amount for each frequency component is obtained. The change of amount is the maximal intensity, which each DCT coefficient can be embedded into suitable capacity of watermarking information. The choice of embedding the watermark into DCT coefficient is exactly depended on the three factors - the frequency position, the magnitude of DCT coefficient, and the amount of embedding information. Secondly, a dynamic watermarking technique is considered. The original image is divided into several non-overlapped blocks, and their corresponding block content feature is computed. According to the different content feature of each block, the maximal amount of watermarking information for each block is embedded into the original image. The multi-watermarking technique is another main point approached in this paper. We proposed a rule of complementary correction that can overcome the drawback of single watermarking technique limited by the similarity of watermark depended upon the amount of cropped image. The veracity of extracted watermark that we analyze depends upon the characteristics coming from the change of coefficient because of doing signal-processing operations. Then we can correct its watermark with help of the other groups. The small amount of cropped image caused by cropping attack can still be extracted of embedded watermark.
In summary, our watermarking scheme applies human visual system to frequency domain and makes sure that embedded watermark into images is invisible in spatial domain. Besides, we proposed the location strength model and the rule of complementary correction to make the embedded watermark high in robustness to resist artificial attacks. Experiment results showed that the proposed scheme demonstrated good performance of robustness against signal-processing attacks, such as JPEG compression, cropping, adding noise and blurring.
摘要 i
英文摘要 iii
誌謝 v
第一章簡介 1
1.1浮水印的要求 1
1.2浮水印的分類 2
1.3相關研究 6
1.3.1 空間域的浮水印技術 6
1.3.2 頻域的浮水印技術 9
1.4研究動機 16
第二章基本理論介紹 18
2.1人類視覺系統模型 18
2.1.1 空間域的JND模型 21
2.1.2 頻域的JND模型 22
2.2浮水印的攻擊種類 22
2.2.1 強健性攻擊 23
2.2.2 存在性攻擊 23
2.2.3 解釋性攻擊 24
2.2.4 合法性攻擊 24
2.3亂數產生器 24
2.4相似度的評估 29
2.5浮水印影像的品質評估 31
第三章基於模擬攻擊法的浮水印技術 33
3.1浮水印系統所使用的技術 34
3.1.1 模擬攻擊單元 34
3.1.2 頻域JND計算單元 39
3.1.3 位置強度模型 42
3.2嵌入浮水印的演算法 46
3.3取出浮水印演算法 50
3.4實驗結果 51
第四章基於區塊分類的浮水印技術 65
4.1區塊分類 66
4.1.1 區塊分類演算法 66
4.1.2 區塊的特性 69
4.1.3 重估可修改的資訊量 70
4.2嵌入浮水印的演算法 71
4.3取出浮水印演算法 74
4.4實驗結果 74
第五章具錯誤更正的多重浮水印技術 93
5.1浮水印偵錯分析法則 93
5.2嵌入浮水印的演算法 99
5.3取出浮水印演算法 102
5.4實驗結果 104
第六章結論與討論 123
參考文獻 130
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