臺灣博碩士論文加值系統

近年隨著網路的蓬勃發展及電腦科技的進步，數位版權也日益重要。當遭到外力破壞，脆弱式浮水印即刻消失，而強健式浮水印則仍可取出可辨識的浮水印。本論文結合脆弱式浮水印及強健式浮水印的特點，提出三個竄改影像的檢測與復原的方法。
第一個方法將原始影像做離散小波轉換(Discrete Wavelet Transformation)後得到的低頻部分去除最低位元(LSB)，再將之轉換為QR Code。QR Code具有25%的錯誤糾正能力，因此可以提高資訊的安全性。最後利用toral automorphism方法攪亂QR Code並將之藏入原始影像的頻域。此一作為大幅增加了藏匿資料的安全性。實驗結果證明，當浮水印影像遭到外力溫和破壞時本方法可檢測到被破壞的區域，而破壞嚴重時本方法則檢測出浮水印失效。實驗證明，Lena、Natural、Road、Mountain及Waterfall藏入檢測資料後，PSNR值分別為39.24 dB、39.36 dB、39.01 dB、39.36 dB及39.41 dB.
第二個方法，利用SPIHT(Set Partitioning in Hierarchical Tree)小波壓縮法壓縮影像的重要區域(Region of Importance)後，以toral automorphism方法將之藏入影像的剩餘不重要區域。此一作為大幅增加藏匿資料的安全性。實驗結果證明本方法可以還原被溫和竄改的重要區域，而在大幅破壞下，則檢測出不可信任影像。實驗證明，犯罪現場、蒙娜麗莎、F-16及藝術品藏入浮水印後，PSNR值分別為39.72 dB、40.03 dB、39.00 dB及39.13 dB。上述影像被竄改後，將其還原，PSNR值分別為37.91 dB、38.17 dB、38.34 dB及38.78 dB。使用Lee方法與本論文提出方法還原被竄改影像，PSNR值分別為36.39 dB與38.58 dB。
第三個方法將浮水印方法應用於身分識別，將影像重要資訊轉為QR Code後，將之印於影像右下角做為一可見式浮水印，以方便使用者利用手持裝置讀取之。另外，利用MCDMA(Modulation of the Code Division Multiple Access)與DSSS(Direct Sequence Spread Spectrum)法將同一QR Code轉化為位元流後隨機藏匿於影像中作為一不可見浮水印。如此作為大幅增加藏匿資料的安全性與強健性。實驗結果證明，利用手持設備或光學讀取器便可輕易讀取影像資訊，而當條碼受到外力破壞，利用此一方法便可將原始條碼恢復。實驗證明，經JPEG壓縮破壞後，本論文所提方法仍可以取出QR code並正確辨識出隱藏文字的訊息。Huang方法與我們所提方法得到的浮水印影像，其PSNR值分別為22.81 dB與39.91 dB。
本論文的浮水印技術，不單藏匿重要資訊，更利用其作破壞檢測與復原。本論文提出的方法可應用於犯罪現場證物保存(實驗一、實驗二)及醫學影像或藝術品資訊儲存(實驗三)。

In the recent years, as the internet flourishes and computer techniques progress, the digital copyright is increasingly important. A fragile watermark suffers slight attack, but a robust watermark can endure heavy damage. This thesis proposed three methods by combining fragile and robust watermarking features. In the first approach, the host image is first transformed by the use of the discrete wavelet transform (DWT). After discarding two least significant bits (LSB) the lower frequency part is transformed into a Quick Response code (QR code) with 25% error correction capacity. Finally, the QR code is embedded into the host image in the frequency domain by using toral automorphism to hash the discrete cosine transform (DCT) block. The experimental results show that if the attack is mild the tampered area of the watermarked image can be detected. But when serious damage occurs the watermarked image becomes invalid. The peak signal-to-noise ratios (PSNRs) of the Lena, Natural, Road, Mountain and Waterfall after watermarking are 39.24 dB, 39.36 dB, 39.01 dB, 39.36 dB and 39.41 dB, respectively.
In the second approach, the region of importance (ROI) of the host image is compressed by the use of set partitioning the in hierarchical tree (SPIHT) wavelet compression method. Then it is embedded into the remaining area of the host image in the frequency domain by troal automorphism. This way, the security of the watermarked image is increased. The experimental results show that the proposed method can detect and recover mild damages in the ROI area. But when serious damage occurs the watermarked image becomes invalid. The PSNRs of Crime scene, Mona Lisa, F-16 and Patting after watermarking are 39.72 dB, 40.03 dB, 39.00 dB and 39.13 dB, respectively. And the PSNRs of the images recovered from the tampered above-mentioned images are 37.91 dB, 38.17 dB, 38.34 dB and 38.78 dB, respectively. The PSNRs of the image recovered from the tampered images by using the Lee’s method and our proposed method are 36.39 dB and 38.58 dB, respectively.
In the third approach, we applied watermarking to identification recognition. The image's information is first transformed to a QR code and then visibly embedded in the corner of the host image so that, it can be easily extracted by mobile devices. Simultaneously, a same but invisible watermark is embedded in the host image by using code division multiple access and direct sequence spread spectrum techniques. Experimental results show that if the barcode has external damage the invisible watermark can be extracted and used to recover the damaged barcode. The simulation result proved the QR code extracted from JPEG attacked host images can still identify the text label. The PSNRs of the watermarked images using the Huang’s method and our proposed are 22.81 dB and 39.91 dB, respectively.
In this thesis, the watermark technique is not only used to hide secret information but also applied to tampered area detection and recovery. The three methods proposed in the thesis can be applied to anticounterfeiting for archives, digital right management and protection of crime scene photos, etc.

Abstract I
摘要 IV
誌謝 VI
Contents VII
List of Tables X
List of Figures XI
Chapter 1 Introduction 1
1.1. Motivation 1
1.2. Related Research 2
1.3. An Overview of the Thesis 4
1.4. Organization of the Thesis 5
Chapter 2 Technique and Applications of Image Processing 6
2.1. Introduction 6
2.2. Spatial Domain 8
2.3. Frequency Domain 8
2.3.1. Discrete Cosine Transformation 9
2.3.2. Discrete Wavelet Transformation 11
Chapter 3 Image Tamper Detection Scheme Using QR Code 15
3.1. Introduction 15
3.2. QR Code 16
3.2.1. Architecture of QR Code 16
3.2.2. QR Code Encoding 17
3.3. Embedding Algorithm 18
3.3.1. DWT and Thumbnail Selection 19
3.3.2. Removing of the QR Code’s Regular Area 20
3.3.3. Dimension Reduction of QR Code 21
3.3.4. Toral Automorphism and Block Selection 22
3.3.5. Coefficient Selection 23
3.3.6. Secret Bit Embedding 24
3.4. Tamper Detection Algorithm 25
3.4.1. Bitstream Extraction and Dimension Recovery 26
3.4.2. Inverse Toral Automorphism 27
3.4.3. Regular Area Padding of the QR code 27
3.4.4. Tamper Detection 28
3.5. Experimental Results 29
3.6. Summary 36
Chapter 4 Image Tamper Detection and Recovery Using SPIHT method 37
4.1. Introduction 37
4.2. Embedding Algorithm 38
4.2.1. Set Partitioning in Hierarchical Tree Compression 39
4.2.2. Region of Importance 42
4.2.3. Toral Automorphism 43
4.2.4. Block Selection of DCT 44
4.2.5. Secret Bits Embedding 45
4.3. Detection and Recovery Algorithm 46
4.3.1. Extracting the Bitstream 47
4.3.2. Set Partitioning in Hierarchical Tree Decompression 48
4.3.3. Inverse Toral Automorphism 48
4.3.4. Tamper Self-Detection and Self-Recovery 48
4.4. Experimental Results 51
4.5. Summary 59
Chapter 5 An Identification Recovery Scheme Using the QR Code 60
5.1. Introduction 60
5.2. Secret Data Embedding 61
5.2.1. Removing Regular Area of the QR Code 62
5.2.2. Direct Sequence Spread Spectrum 63
5.2.3. Modulation of the Modified Code Division Multiple Access 64
5.2.4. DCT and Block Selection 65
5.3. Detection and Recovery Process 67
5.3.1. Demodulation of the Modified Code Division Multiple Access 68
5.3.2. Inverse Direct Sequence Spread Spectrum 71
5.3.3. Regular Area Padding of the QR Code 71
5.3.4. Watermark Verification Process 72
5.4. Experimental Results 73
5.5. Summary 83
Chapter 6 Conclusion 84
References 86
List of Publications 99

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