||The Research on Digital Watermarking and Image Recovery
||Department of Electrical Engineering
||In recent years, Internet is an excellent distribution system for digital media because it is cheap and fast. It is known that digital media can be easily replicated and modified. Thus, digital watermarking has been developed to protect the copyright and integrity of multimedia content. A watermarking scheme can be classified as either robust watermarking or fragile watermarking. Robust watermarking is designed for the copyright and ownership verification. Fragile watermarking is used for image authentication and tamper detection, which can be used to identify any alteration of the images.
In parallel to watermarking there was also another field of study, image inpainting. Image inpainting technique aims to fill in the damaged region of an image with new information so that it is hard to find that the image has once been damaged. Image inpainting is extensively used in various fields such as photo editing, cultural relic restoration, data compression and network data transmission.
The first issue of this dissertation is to introduce a robust watermarking system based on Integer Discrete Cosine Transform. The 4x4 IntDCT transform can reduce blocking artifacts resulted from 8x8 DCT transform. The integer transform is designed such that the transformation involves only additions and shift operations, and no mismatch exists between the forward and inverse transforms. This reduces the computational complexity and it is much easier for hardware implementation.
The second issue of this dissertation is to introduce a new invariant semi-fragile digital watermarking technique based on eigenvalues and eigenvectors of a real symmetric matrix generated by the four pixel-pair. And the multi-rings Zernike transform (MRZT) is proposed to achieve geometric invariance. A signature bit for detecting malicious tampering of an image is generated using the dominant eigenvector. The MRZT method is against the geometric distortions even when the image is under malicious attacks.
In the third issue of this dissertation, we combine the algorithms of watermarking, halftoning and inverse halftoning to propose a novel image inpainting technique. This technique use LSB method to embed halftone image into original image for protecting the image. In image repair process, we use LSB method to extract the halftone information, and the reference image is achieved from LUT inverse halftoning. Finally we use the reference image to finish the image inpainting work. Experiment shows the performance of our method is very excellent in image inpainting.
In the last issue of this dissertation, a novel scheme of Automatic image authentication and recovery has been proposed, in which the signature bits for detecting malicious tampering of an image are generated using the dominant eigenvector. To achieve high-quality image recovery, we also insert the binary halftone image converted from the original image into the watermarked image to obtain the protected watermarked image. If the image has been tampered, the altered region of the image can be detected and recovered in a fully automated fashion.
List of Figures IV
List of Tables VII
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related works 1
1.2.1 Watermarking 1
1.2.2 Image Inpainting and Recovery 4
1.3 Main Contributions of the Proposal 6
1.4 The Organization of this Dissertation 7
CHAPTER 2 Proposed Robust Watermarking Technique Using IntDCT Based AC Prediction 8
2.1 Introduction 9
2.2 IntDCT and AC Prediction 10
2.2.1 Integer Discrete Cosine Transform 10
2.2.2 Data Embedding Technique Based on the Prediction of AC Coefficients 12
18.104.22.168 AC Prediction 12
22.214.171.124 Yulin Wang and Alan Pearmain’s Watermark Embedding Technique 14
2.3 The Proposed Robust Watermarking Scheme Using IntDCT and AC Prediction 14
2.3.1 Embedding the Watermark 14
2.3.2 Extracting the Watermark 17
2.4. Experimental Results 20
2.4.1 Imperceptibility and Watermark Capacity 22
2.4.2 Image Processing and Geometrical Attacking 23
2.4.3 JPEG Compression Attacks 24
2.5 Summary 25
Chapter 3 Proposed Geometric Invariant Semi-fragile Watermarking Using Real Symmetric Matrix 26
3.1. Introduction 26
3.2 Multi-rings Zernike Transform 29
3.2.1 Zernike Transform 29
3.2.2 Candidate Selection Process 32
3.3. Eigenvectors and Eigenvalues of Real Symmetric Matrix 33
3.4 The Proposed Semi-fragile Watermarking Scheme 35
3.4.1 Embedding Algorithm 35
3.4.2 Extraction Algorithm 37
3.5. Experimental Results 39
3.5.1 Rotation and Scaling Invariance 39
3.5.2 Image Quality 40
3.5.3 Dimension Decision of the Real Symmetric Matrix 42
3.5.4 Comparison 43
3.5.5 Image Authentication 45
3.6 Summary 49
Chapter 4 Proposed Image Inpainting Scheme Based on Watermarking and Halftoning 50
4.1 Introduction 50
4.2 Related Works 52
4.2.1 Least Significant Bit Substitution 52
4.2.2 Error Diffusion Algorithm for Halftoning 53
4.2.3 Look-Up-Table Inverse Halftoning 55
4.3 The Proposed Image Inpainting Scheme 58
4.4 Experimental Results 60
4.5 Summary 69
Chapter 5 Proposed Automatic Image Authentication and Recovery Using Multiple Watermarks 70
5.1 Introduction 71
5.2 Fragile Watermark for Image Authentication 73
5.3.1 Embedding Algorithm 73
5.3.2 Extracting Algorithm 74
5.4 The Proposed Automatic Image Authentication and Recovery Scheme 75
5.4.1 Fragile Watermark Embedded process 75
5.4.2 Damaged Image Recovery Process 76
5.5 Experimental Results 78
5.5.1 The Results of Watermark Embedding phase 78
5.5.2 The Results of Image Inpainting phase 78
5.6 Summary 82
CHAPTER 6 Conclusions and Future Works 83
6.1 Conclusions 83
6.2 Future Works 84
List of Figures
Fig. 2.1 Frequency Distribution in a DCT block (left), Structural decomposition in a DCT block(right) 13
Fig. 2.2 Neighbourhood of DCT blocks used for AC prediction 13
Fig. 2.3 the average PSNR of the ten natural images 16
Fig. 2.4 the procedure of embedding the watermark 16
Fig. 2.5 the procedure of extracting the watermark 19
Fig. 2.6 Nine 512x512 host images 21
Fig. 2.7 watermark (64x64) 21
Fig. 2.8 The correct decoding Rates of extracting watermarks 24
Fig. 2.9 The correct decoding rates under attacks of different JPEG quality compression 24
Fig. 3.1 Block diagram of proposed digital watermark embedding system. 37
Fig. 3.2 Block diagram of proposed digital watermark extraction system. 37
Fig. 3.3 (a) Original Lena image. (b) Watermarked Lena image. 42
Fig. 3.4 (a) Modified Pepper image. (b) Modified areas detected by proposed method. (c) Modified areas detected by Lin  method. 47
Fig. 3.5 (a) Modified Lena image. (b) Modified areas are detected by proposed method. (c) Modified areas are detected by Lin  method. 47
Fig. 3.6 (a) Modified Baboon image. (b) Modified areas are detected by proposed method. (c) Modified areas are detected by Lin  method. 47
Fig. 3.7 (a) Rotated Baboon image. (b) Simulated result of (a). Modified areas are detected by proposed method. 48
Fig. 3.8 (a) Watermarked natural image. (b) Modified natural image. (c) Modified areas are detected by proposed method. (d) Modified areas are detected by Lin  method. 48
Fig. 4.1 The error diffusion algorithm. 53
Fig. 4.2 Error filters of different error diffusion methods. 55
Fig. 4.3 LUT template 57
Fig. 4.4 the process of watermark embedded phase. 58
Fig. 4.5 the process of inpainting phase 59
Fig. 4.6 watermark embedded phase (a)original image Lena(512x512) (b) the halftone image converted from Fig. 4.6(a)with error diffusion method. (c) the permuted binary random image converted form Fig. 4.6(b). (d) the protected image which is embedded Fig. 4.6(c) into Fig. 4.6(a),PSNR=54.14. 62
Fig. 4.7 An example of inpainting phase (a)The damaged Lena image attacked in both eyes from the protected image(Fig. 4.6(d)) (b) The random binary damaged image from Fig. 4.7(a) with LSB method. (c) The damaged halftone image converted from Fig. 4.7(b). (d) The reference image converted from Fig. 4.7(c) with LUT inverse halftoning algorithm. (e) The enhanced reference image form Fig. 4.7(d) with median filtering. (F) The restored image. 63
Fig. 4.8 (a) Damaged rate is 1.76% for Lena; (b) Restored image, PSNR=47.80 64
Fig. 4.9 (a) Damaged rate is 1.35% for Lena; (b) Restored image, PSNR=48.82 64
Fig. 4.10 (a) Damaged rate is 2.09% for Lena; (b) Restored image, PSNR=47.25 65
Fig. 4.11 (a) Damaged rate is 5.46% for Lena; (b) Restored image, PSNR=41.12 65
Fig. 4.12(a) Damaged rate is 10.74% for Lena; (b) Restored image, PSNR=34.17 66
Fig. 4.13 (a) Damaged rate is 41.07% for Lena; (b) Restored image, PSNR=16.69 66
Fig. 4.14 (a) Damaged rate is 64.85% for Lena; (b) Restored image, PSNR=10.69 67
Fig. 4.15 Experimental results from utilizing different methods: (a)The damaged image; (b) The PDE method.; (c) The PII method. ;(d) Our proposed method. 68
Fig. 4.16 Zoom-in inpainting result of Fig. 4.15 (a), 4.15 (c) and 4.15(d): (a) The damaged region; (b) The PII method ; (c) The proposed method. 68
Fig. 5.1 Block diagram of proposed digital fragile watermark embedding system. 74
Fig. 5.2 Block diagram of proposed fragile watermark extracting system. 75
Fig. 5.3 watermark embedded phase. 76
Fig. 5.4 Inpainting phase. 77
Fig. 5.5 (a) The original image for Lena; (b)The watermarked image after embedding signature bits into Fig.5.5 (a), PSNR=46.2043; (c) The protected image after embedding the halftone image into Fig. 5.5(b), PSNR=44.82. 79
Fig. 5.6 (a)The original image for Pepper; (b)The watermarked image after embedding signature bits into Fig.5.6 (a), PSNR=46.2043; (c) The protected image after embedding the halftone image into Fig. 5.6(b), PSNR=44.97. 79
Fig. 5.7 (a) The damaged image for Lena; (b) Auto localize the tampered areas from Fig 5.7(a); (c) The result of inpainting from Fig.5.7 (a), PSNR=43.67. 80
Fig. 5.8 (a)The original image for Pepper; (b) Tampered image from Fig. 5.8(a); (c) Auto localize the tampered areas from Fig 5.8(b); (d) The result of inpainting, PSNR=30.74. 80
Fig. 5.9 (a)The original image for Pepper; (b)The watermarked image after embedding signature bits into Fig.5.9 (a),PSNR=46.17; (c)The protected image after embedding the halftone image into Fig. 5.9(b),PSNR=44.80. 81
Fig. 5.10 (a) The altered image for Door; (b) Auto localize the tampered areas from Fig 5.10(a); (c)The recovered image obtained by our scheme, PSNR=42.95; (d) The recovered image obtained by Wang’s scheme, PSNR=38.54. 81
List of Tables
Table 2.1 Imperceptibility using PSNR 22
Table 2.2 Watermark capacity(bit) Image Size 512*512 23
Table 2.3 Quality of watermarked image, PSNR (db). 23
Table 3.1 Signature bits and direction of dominant eigenvector. (The dimension of the real symmetric matrix is 2×2) 34
Table 3.2 The MSE of the estimating angle under malicious attacks 40
Table 3.3 Estimating angle by Zernike transform and proposed multi-ring Zernike transform 40
Table 3.4 PSNR value with different dimension of the real symmetric matrix 43
Table 3.5 Bit error rate of images under different quality of JPEG compression 44
Table 3.6 Bit error rate of proposed extracting method under different quality of JPEG compression 44
Table 3.7 PSNR value of watermarked image 44
Table 3.8 Bit error rate for various embedding algorithms 45
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