現今資訊科技發展快速，使得生活中相對便利許多，人們可以很輕易的在網路上下載各種多媒體資訊，例如圖像、聲音及影片，然而在數位化的時代，複製及竄改多媒體資訊亦是一件輕而易舉之事，利用非法管道上下載他人多媒體資訊的事件每每層出不窮，因此保護智慧財產權與資訊安全是相當重要的議題。
本文提出一種適用於二維碼(QR code)之數位浮水印技術，位元資訊藏匿於離散小波轉換(discrete wavelet transform, DWT)及離散餘弦轉換(discrete cosine transform, DCT)之混合域中，另結合自適性嵌入強度調整，以增進浮水印嵌入效能。DWT與DCT均是將影像從空間域投射到頻域的轉換，DWT同時具有時域與頻域的多重解析能力，DCT則是呈現不同頻率成分之能量分布，該特性可作為浮水印嵌入對象的篩選；而位元資訊的嵌入方式係採漸進式量化索引調變(progressive quantization index modulation, PQIM)，使得數位浮水印的強健性能在視覺品質不受影響的前提下獲得提升。
所研發之浮水印技術是以十五種不同的攻擊進行性能測試，其中包含JPEG壓縮、JPEG2000壓縮、高斯雜訊、椒鹽雜訊、中值濾波、高斯低通濾波、縮放攻擊、旋轉攻擊、剪裁攻擊、亮度調整等，並與另兩篇文獻所載之方法互作比較。實驗結果顯示，除了在椒鹽攻擊部分，大多數攻擊（例如JPEG壓縮、高斯雜訊）都是由本文所提出之DWT-DCT (4×4)方法勝出。

Nowadays information technology has been growing rapidly, and a good use of this technology makes human life more convenient. People can easily download various multimedia data, such as image, sound and video, via the network. While it is easy to copy and modify multimedia pieces in the digital era, the events of illegally plagiarizing other’s media work also occur frequently. Therefore, it is very important to safeguard information security and protect creator’s intellectual property.
This thesis presents a digital watermarking scheme suitable for QR code images, where binary information is hidden in the hybrid domain jointly formed by the discrete wavelet transform (DWT) and discrete cosine transform (DCT). The incorporation of adaptive embedding into the watermarking scheme makes it possible to improve the efficiency. Both DWT and DCT can cast images from the spatial to spectral domain. DWT holds multiresolution analytical capability in space and frequency, while DCT is competent in portraying the energy distribution of frequency components. These characteristics can be exploited to identify suitable objects for watermarking applications. Binary embedding is implemented using a novel technique called progressive quantization index modulation (PQIM), which enhances the robustness of digital watermarks without affecting the visual quality.
The robustness of the proposed watermarking scheme is evaluated using fifteen different attacks, consisting of JPEG compression, JPEG2000 compression, Gaussian noise, salt-and-pepper noise, median filtering, Gaussian lowpass filtering, scaling, rotation correction, cropping, and brightness adjustment. The performance is compared with those obtained from two other methods in the literature [25, 26]. Experiment results indicate that, except in the case of salt-and-pepper noise attack, the proposed scheme, which is designated as “DWT-DCT(4x4)”, can effectively resist most of the attacks (including JPEG compression and Gaussian noise).

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究動機 1
1.2 浮水印簡介 1
1.3 數位浮水印架構 3
1.3.1 數位浮水印嵌入 3
1.3.2 數位浮水印提取 4
1.3.3 數位浮水印嵌入領域 4
1.4 QR code簡介 5
1.5 在QR code中嵌入數位浮水印的試探 6
1.6 論文架構 7
第二章 背景知識 8
2.1 離散餘弦轉換(Discrete cosine transform) 8
2.2 離散小波轉換(Discrete wavelet transform) 10
2.3 Arnold transform 12
2.4 量化索引調變 12
第三章 文獻探討 13
3.1 文獻[25]嵌入及提取流程圖 14
3.2 文獻[26] 嵌入及提取流程圖 17
第四章 數位浮水印之架構 20
4.1 前言 20
4.2 灰階化方式 21
4.2.1 亂數灰階QR code影像 21
4.2.2 固定灰階QR code影像 23
4.2.3 漸層灰階QR code影像 24
4.3 二元資訊的嵌入與提取 25
4.4 數位浮水印嵌入流程 26
4.5 數位浮水印提取流程 29
第五章 實驗結果與分析 30
5.1 實驗素材 30
5.1.1 數位浮水印 30
5.1.2 QR code影像 30
5.2 評估指標 32
5.3 測試的各種類型攻擊 33
5.4 灰階程度分析 34
5.5 與相關文獻相互比較 37
第六章 結論 47
參考文獻 48

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