臺灣博碩士論文加值系統

數據隱藏技術（Data Hiding）屬於資訊安全的研究領域，是一種將秘密訊息嵌入至文字、音訊、圖像與影片等掩護媒體中的偽裝技術，藉由偽裝媒體的掩護以實現安全通訊之目的。本文以圖像方面之數據隱藏為研究主題，並根據圖像載體種類，分別於圖像的“像素域”與“壓縮域”提出數據隱藏技術。
本文於“像素域”方面提出了一種基於龜殼參考矩陣的馬賽克圖像雙層數據隱藏技術。過程中，我們首先使用三張不同亮度的咖啡豆圖像生成馬賽克瓷磚庫，藉由咖啡豆瓷磚的拼貼以模擬給定的主圖像，製作出主要輪廓為主圖像，但細節紋路為咖啡豆的馬賽克圖像。其次，以馬賽克圖像作為數據隱藏的掩護載體，根據龜殼參考矩陣在馬賽克圖像的咖啡豆層級，藉由替換相近的咖啡豆瓷磚以嵌入第一層秘密訊息。最終，於馬賽克圖像的像素層級嵌入第二層秘密訊息。由於馬賽克圖像的低視覺質量，利用新提出的偏移遮罩以及強嵌入和弱嵌入的概念，在馬賽克圖像的像素層級嵌入3 bpp的秘密訊息後其PSNR值可提高約1.5 dB，將數據隱藏應用於馬賽克掩護圖像是一個全新的想法，而且很有前途。
在“壓縮域”中，向量量化（Vector Quantization，VQ）是一種流行的數位圖像壓縮技術，其壓縮結果（索引表）還可以使用側邊匹配向量量化（Side Match Vector Quantization，SMVQ）進一步壓縮。本文提出了兩種應用於VQ索引表的可逆數據隱藏（Reversible Data Hiding，RDH）方案。第一種是基於主成分分析（Principal Component Analysis，PCA）的VQ索引表可逆數據隱藏方案。該方案首先基於PCA算法對VQ的編碼本進行的排序，增加編碼本相鄰索引的相關性，以形成排序編碼本。其次，使用PCA編碼本對圖像進行VQ壓縮，生成VQ索引表。此時，VQ索引表成為RDH的掩護載體。最後，通過利用自然圖像的連續性特徵，可以進一步壓縮 VQ 索引碼，以騰出空間來嵌入秘密訊息。壓縮方法使用差異預測壓縮，其參考聯合相鄰編碼（Joint Neighboring Coding，JNC）數據隱藏方案，利用兩個相鄰索引對之間的差異進行壓縮編碼，不同之處在於我們的編碼方向統一使用橫向或直向的光柵掃描處理，並且只在可壓縮索引中嵌入秘密訊息。實驗結果表明，所提方法可以有效改善JNC根據秘密訊息選擇相鄰壓縮編碼索引所導致的壓縮率差的問題。壓縮結果與其他使用狀態編碼本的壓縮方案有異曲同工之妙，但在算法上更加精簡。
第二種方案是基於反聚類（De-Cluster）規則的SMVQ可逆數據隱藏。在該方案中，反聚類則應用於SMVQ的可壓縮索引的主編碼本與不可壓縮索引的狀態編碼本。在編碼本中，根據反聚類規則將索引進行一對一映射分組，從而在SMVQ壓縮過程中，通過替換映射索引來達到嵌入秘密訊息的目的。所提出的方案可以生成具有高載荷的偽裝VQ索引表。此外，我們的方案減少了傳統SMVQ欲恢復VQ索引表所需的許多指示位元，提高了壓縮質量，結果表明，在圖像壓縮率和嵌入率方面與先進的RDH方法相比有很好的結果。

Data hiding is a hot research topic in information security. It is a camouflage technology that embeds secret messages in cover media such as text, audio, images and videos, the purpose of secure communication is achieved by camouflaging the cover media. This research focuses on data hiding for images. According to the type of image carrier, data hiding techniques in the "plain image domain" and "compressed image domain" are proposed.
In the "plain image domain", we propose a two-layer data hiding scheme for mosaic images based on the turtle shell reference matrix. In this scheme, we first use three coffee bean images of different brightness to generate a coffee bean image-tile library. Using the library of coffee bean tiles, we produce mosaic image that looks similar to a given original image but the detail texture is coffee beans. Secondly, the mosaic image is used as a cover carrier for the data hiding. Replacing similar coffee bean tiles based on the turtle shell reference matrix to embed secret messages at the tile level in the first layer of data hiding. Finally, we embed secret message into the pixel level in the second layer of data hiding. To improve the visual quality of the mosaic image, we embed secret data using the newly proposed polarized search mask and the concepts of strong and weak embedding. PSNR value of the mosaic image can be improved by about 1.5 dB after embedding 3 bpp at the pixel level. Data hiding for the mosaic cover images is a new idea and it is promising.
In the "compressed image domain", Vector Quantization (VQ) is a popular digital image compression technology. Its compression result (index table) can be further compressed using Side Match Vector Quantization (SMVQ). We propose two Reversible Data Hiding (RDH) schemes for VQ index table. The first is a reversible data hiding scheme based on Principal Component Analysis (PCA). In the proposed scheme, the codebook of VQ is firstly sorted based on PCA to increase the correlation of adjacent indices in the codebook to form a state codebook. Secondly, the PCA state codebook is used to VQ compress the image into a VQ index table. The VQ index table is treated as the cover carrier of the RDH. Finally, by leveraging the continuity feature of natural images, a VQ index code can be further compressed to spare room for embedding secret data. The difference prediction compression is applied. The prediction method refers to the Joint Neighboring Coding (JNC) data hiding scheme, and uses the difference between two adjacent index pairs for compression encoding. The difference is that our encoding direction uniformly uses horizontal or straight raster scan processing, and only embeds secret messages in compressible indices. Experimental results show that the proposed method can effectively improve the poor compression ratio caused by JNC selecting adjacent compression coding indices according to the secret message. The compression results are similar to other compression schemes using the state codebook, but the algorithm is more streamlined.
The second scheme is a reversible data hiding for SMVQ based on de-cluster rules. In this scheme, the de-cluster rules are applied to the main codebooks of compressible indices and state codebooks of incompressible indices for SMVQ. In the codebook, based on de-cluster rules, one-to-one group mapping is performed for the codewords, so that during SMVQ compression, the purpose of embedding the secret message is achieved by replacing the mapping indices. The proposed scheme can generate a camouflaged VQ index table with high payload. In addition, our scheme reduces many indication bits required by traditional SMVQ to restore the VQ index table and improve the compression quality. In terms of image compression rate and embedding rate, our scheme has good results compared with advanced RDH methods.

目錄
致 謝 I
摘 要 II
ABSTRACT IV
目錄 VII
表目錄 VIII
圖目錄 X
第壹章、緒論 1
第一節 研究動機與背景 1
第二節 研究方法 4
第三節 論文架構 5
第貳章、圖像與數據隱藏基本介紹 6
第一節 數位圖像 6
第二節 圖像數據隱藏概述 9
第參章、基於龜殼參考矩陣之馬賽克圖像雙層數據隱藏 10
第一節 相關研究 10
第二節 應用於馬賽克圖像之龜殼矩陣數據隱藏方案 12
第三節 結果與討論 22
第肆章、基於PCA之VQ索引圖像可逆數據隱藏方案 34
第一節 相關研究 34
第二節 基於主成分分析的VQ索引表之相鄰差值壓縮可逆數據隱藏方案 39
第三節 結果與討論 42
第伍章、基於反聚類規則的 SMVQ 壓縮索引圖像可逆數據隱藏 53
第一節 相關研究 53
第二節 基於反聚類規則的 SMVQ 壓縮索引圖像可逆數據隱藏 57
第三節 結果與討論 62
第陸章、結論 71
參考資料 72

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