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研究生:Dwi Riyono
研究生(外文):Dwi Riyono
論文名稱:Study and Improvement on Image Security and Retrieval
論文名稱(外文):Study and Improvement on Image Security and Retrieval
指導教授:郭景明郭景明引用關係
指導教授(外文):Jing-Ming Guo
口試委員:Jar-Ferr YangChung-Nan LeeHeri PrasetyoProf. Jian-Jiun DingJing-Ming Guo
口試委員(外文):Jar-Ferr YangChung-Nan LeeHeri PrasetyoJian-Jiun DingJing-Ming Guo
口試日期:2019-01-29
學位類別:博士
校院名稱:國立臺灣科技大學
系所名稱:電機工程系
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:2019
畢業學年度:107
語文別:英文
論文頁數:213
中文關鍵詞:beta chaoticimage encryptionsecret sharinghyperchaosimage watermarkingpermutationimage retrievalbitmap featurebit probability
外文關鍵詞:beta chaoticimage encryptionsecret sharinghyperchaosimage watermarkingpermutationimage retrievalbitmap featurebit probability
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In this dissertation, we conduct three researches : 1) improved beta chaotic image encryption for multiple secret sharing, 2) hyperchaos permutation on false-positive-free SVD-based image watermarking, and 3) a novel image descriptor based on dot-diffused block truncation coding with bit probability.
The first part is related to image encryption. The proposed image encryption is further extended and applied for the (n,n)-MSS task. Some ideas have been delivered to solve the problem in former existing scheme on dealing with odd number of secret images. In the (n,n)-MSS, the proposed method utilizes an additional random image, k image encryption, and two different masking coefficients to overcome the aforementioned problem. Experimental results validate the usability and effectiveness of the proposed method in the image encryption as well in (n,n)-MSS system. Thus, the proposed method can be regarded as very competitive candidate in the image encryption and secret sharing applications.
For the second part, some extensive experiments on image watermark scheme have been reported. It investigates the effect of hyperchaos permutation on FPF SVD-based image watermarking scheme. The watermark image is firstly scrambled using the hyperchaos permutation before being embedded into the host image. The proposed method yields a good result in the image watermarking scheme.
The third part presents a novel image descriptor based on DDBTC with bit probability. Specifically, the image feature descriptor is formed by combining the color feature directly derived from a DDBTC encoded data stream with bit probability and bitmap feature derived from the corresponding bitmap image. The color feature effectively captures the image brightness and color distribution, and the bitmap feature characterizes the image content. To demonstrate its viability as an image descriptor, its application to image retrieval and classification are considered. Results confirm the superiority of the proposed image descriptors in terms of the image retrieval and classification tasks for both natural and textural image databases, for both color and grayscale images.
In this dissertation, we conduct three researches : 1) improved beta chaotic image encryption for multiple secret sharing, 2) hyperchaos permutation on false-positive-free SVD-based image watermarking, and 3) a novel image descriptor based on dot-diffused block truncation coding with bit probability.
The first part is related to image encryption. The proposed image encryption is further extended and applied for the (n,n)-MSS task. Some ideas have been delivered to solve the problem in former existing scheme on dealing with odd number of secret images. In the (n,n)-MSS, the proposed method utilizes an additional random image, k image encryption, and two different masking coefficients to overcome the aforementioned problem. Experimental results validate the usability and effectiveness of the proposed method in the image encryption as well in (n,n)-MSS system. Thus, the proposed method can be regarded as very competitive candidate in the image encryption and secret sharing applications.
For the second part, some extensive experiments on image watermark scheme have been reported. It investigates the effect of hyperchaos permutation on FPF SVD-based image watermarking scheme. The watermark image is firstly scrambled using the hyperchaos permutation before being embedded into the host image. The proposed method yields a good result in the image watermarking scheme.
The third part presents a novel image descriptor based on DDBTC with bit probability. Specifically, the image feature descriptor is formed by combining the color feature directly derived from a DDBTC encoded data stream with bit probability and bitmap feature derived from the corresponding bitmap image. The color feature effectively captures the image brightness and color distribution, and the bitmap feature characterizes the image content. To demonstrate its viability as an image descriptor, its application to image retrieval and classification are considered. Results confirm the superiority of the proposed image descriptors in terms of the image retrieval and classification tasks for both natural and textural image databases, for both color and grayscale images.
Contents
Chapter 1. Introduction……………………………………………………………......1
1.1. Motivation…………………………………………………………………………………....1
1.2. Contribution………………………………………………………………………………..…2
1.3. Organization……………………………………………………………………………..…...4
Chapter 2. Image Encryption for Multiple Secret Sharing........................................6
2.1. Proposed Image Encryption……………………………………………………………..…..17
2.1.1. Proposed Image Confusion using Beta Chaotic Map……………………………......18
2.1.2. Proposed Image Diffusion using Beta Chaotic Maps……………………………….20
2.1.3. Bitwise Exclusive-OR on Encrypted Image…………………………………………21
2.2. Proposed Multiple Secret Sharing…………………………………………………………..26
2.2.1. Proposed Method with Additional Random Image………………………………….28
2.2.2. Proposed Method with k Image Encryption………………………………………....32
2.2.3. Proposed Method with Two Masking Coefficients………………………………….37
2.3. Experimental Results……………………………………………………..…………………43
2.3.1. Performance of Improved Beta Chaotic Image Encryption…………………………44
2.3.2. Effect of Additional Random Image for the Proposed Method………………..........49
2.3.3. Effect of k Image Encryption for the Proposed Method…………………………….53
2.3.4. Effect of Two Different Masking Coefficients for the Proposed Method……………57
2.3.5. Performance Comparisons Against Former Schemes……………………………….73
2.3.6. Effect of Image Stacking…………………………………………………………….82
2.4. Summary……………………………………………………………………………………….84
Chapter 3. Hyperchaos on SVD-Based Image Watermarking………..…………..85
3.1. Hyperchaos Image Permutation……………………………………………………………..87
3.1.1. Hyperchaos Maps…………………………………………………….……………..87
3.1.2. Hyperchaos Image Scrambling……………………………………..……………….91
3.2. Proposed Image Watermarking…………………………………………………….………..97
3.2.1. Singular Value Decomposition………………………………………………….......98
3.2.2. Watermark Embedding………………………………………………………….......99
3.2.3. Watermark Extraction……………………………………………………………...103
3.2.4. Dealing with Color Image………………………………………………………….105
3.2.5. Similarity Measurement…………………………………………………………....107
3.3. Analysis………………………………………………………………………………...….108
3.3.1. False-Positive-Problem…………………………………………………………….108
3.3.2. Embedding Singular Values………………………………………………………..113
3.3.3. Embedding Principal Components…………………………………………………118
3.3.4. Quality of Watermarked Image………………………………………………….....124
3.3.5. Connection to Peak-Signal-to-Noise-Ratio………………………………………...127
3.4. Experimental Results………………………………………………………………………130
3.4.1. Imperceptibility Test……………………………………………………………….132
3.4.2. Robustness Test…………………………………………………………………….134
3.4.3. Performance Evaluation…………………………………………………………....141
3.4.4. Performance on Color Image……………………………………………………....145
3.5. Summary………………………………………………………………………………………150
Chapter 4. Descriptor Image Retrieval DDBTC with Bit Probability..……...…151
4.1. Literature review…………………………………………………………………………..153
4.1.1. DDBTC Image Color Quantizer and Bitmap Image………………………………154
4.1.2. Bit Probability……………………………………………………………………..159
4.2. Proposed image Descriptor………………………………………………………………..160
4.2.1. Color Feature (CF)………………………………………………………………...160
4.2.2. Bitmap Feature (BF)………………………………………………………………165
4.2.3. Histogram Binning………………………………………………………………...167
4.3. Image Retrieval and Classification using DDBTC with Bit Probability………………….168
4.3.1. Image Retrieval…………………………………………………………………..168
4.3.2. Image Classification……………………………………………………………...171
4.4. Experiment Results ……………………………………………………………………….171
4.4.1. Experimental Setup………………………………………………………………..173
4.4.2. Image Retrieval Application………………………………………………………177
4.4.3. Image Classification Application…………………………………………………183
4.4.4. Efficiency Analysis……………………………………………………………….186
4.5. Summary…………………………………………………………………………….……187
Chapter 5. Conclusion…………………………………………………………..188
5.1. Future work………………………………………………………………...189

References………………………………………………………………………...190


















List of Figures
Fig. 2.1. Schematic diagram of the former MSS scheme (a) encoder, and (b) decoder………………………………………………………………………………………………..8
Fig. 2.2. A set of secret images: (a) Baboon, I_1, (b) Lake, I_2, (c) Peppers, I_3, and (d) Barbara, I_4……………………………………………………………………………………………..…..….11
Fig. 2.3. Results of the former MSS scheme with n=3: (a-c) a set of shared images, and (d-f) a set of recovered images………………………………………………………………………...……….14
Fig. 2.4. Illustration of image confusion and diffusion process……………………………….……..18
Fig. 2.5. Result of XOR operations with random image R_1~U(0,50), R_2~U(0,100), and R_3~U(0,255): (a) shuffled image S{I_1 }, (b) I_1⊕R_1, (c) I_1⊕R_2, (d) I_1⊕R_3, (e) S{I_1 }⊕R_2, and (f) S{I_1 }⊕R_3…………………………………………………………………………...…………..25
Fig. 2.6. Schematic diagram of proposed multiple secret sharing over several color images on: (a) encoder, and (b) decoder sides, respetively…………………………………….……………………27
Fig. 2.7. Schematic diagram of the proposed MSS method with additional random image for: (a) encoder, and (b) decoder side………………………………………………………..………………28
Fig. 2.8. Illustration diagram of the proposed MSS method with k image encryption: (a) encoder, and (b) decoder side…………………………………………………………….………………………..34
Fig. 2.9. Process diagram of the proposed MSS method with two different masking coefficients: (a) encoder, and (b) decoder side………………………………………………………………………..38
Fig. 2.10. Examples of proposed image encryption on Babbon image using: (a) only confussion, (b) only diffusion, and (c) combining confussion and diffusion process. The decryption results of images (a-c) using (d-f) correct keys, and (g-i) incorrect keys………………………………………………45
Fig. 2.11. Results of the proposed method with additional random image while n=4: (a-d) a set of shared images {S_1,S_2,S_3,S_4 }, (e-h) a set of recovered secret images {I ̃_1,I ̃_2,I ̃_3,I ̃_4 } using correct chaotic keys, (i-l) a set of recovered secret images {I ̃_1,I ̃_2,I ̃_3,I ̃_4 } using incorrect chaotic keys, (m-n) recovered secret images {I ̃_1,I ̃_2 } using correct keys while n-1 shared images are available, and (o-p) a set of recovered secret images {I ̃_1,I ̃_2 } using correct keys while n-2 shared images are available….....…50
Fig. 2.12. Results of the proposed method with additional random image while n=3: (a-c) shared images {S_1,S_2,S_3 }, (d-f) recovered secret images {I ̃_1,I ̃_2,I ̃_3 } using correct keys, (g-i) recovered secret images {I ̃_1,I ̃_2,I ̃_3 } using incorrect keys, (j-k) recovered secret images {I ̃_1,I ̃_2 } using correct keys while n-1 shared images available, and (l) recovered secret image I ̃_1 using correct key while n-2 shared images available……………………………………………………………………………..52
Fig. 2.13. Results of the proposed method with k=2 image encryption while n=4: (a-d) shared images {S_1,1,S_1,2,S_2,1,S_2,2 }, (e-h) recovered secret images {I ̃_1,1,I ̃_1,2,I ̃_2,1,I ̃_2,2 } using correct chaotic keys, (i-l) recovered secret images {I ̃_1,1,I ̃_1,2,I ̃_2,1,I ̃_2,2 } using incorrect chaotic keys, (m-n) recovered secret images {I ̃_1,1,I ̃_1,2 } using correct keys while nk-1 shared images are available, and (o-p) recovered secret images {I ̃_1,1,I ̃_1,2 } using correct keys while nk-2 shared images are available…54
Fig. 2.14. Results of the proposed method while n=3 and k=2: (a-c) shared images {S_1,1,S_2,1,S_3,1 }, (d-f) recovered images {I ̃_1,1,I ̃_2,1,I ̃_3,1 } using correct chaotic keys, (g-i) recovered images {I ̃_1,1,I ̃_2,1,I ̃_3,1 } using incorrect chaotic keys, and (j-l) recovered image I ̃_1,1 while nk-1, nk-2, and nk-3 shared images, respectively, are available using correct chaotic keys………..56
Fig. 2.15. Results of the proposed method using two masking coefficients while n=4: (a-d) shared images {I_1,I_2,I_3,I_4 }, (e-h) recovered secret images {I ̃_1,I ̃_2,I ̃_3,I ̃_4 } using correct keys, (i-l) recovered secret images {I ̃_1,I ̃_2,I ̃_3,I ̃_4 } using incorrect keys, (m-n) recovered secret images {I ̃_1,I ̃_2 } using correct keys while n-1 shared images are available, (o-p) recovered secret image I ̃_1 using correct keys while n-2 and n-3 shared images are available, respectively………………………………….65
Fig. 2.16. Results of the proposed method using two masking coefficients while n=3: (a-c) shared images {S_1,S_2,S_3 }, (d-f) recovered secret images {I ̃_1,I ̃_2,I ̃_3 } using correct keys, (g-i) recovered secret images {I ̃_1,I ̃_2,I ̃_3 } using incorrect keys, (j-k) recovered secret images {I ̃_1,I ̃_2 } using correct keys while n-1 shared images are available, and (l) recovered secret image I ̃_1 using correct key while n-2 shared images are available……………………………………………………………...…………..72
Fig. 2.17. Overlying two shared images: (a-b) S_1⊕S_2 and S_2⊕S_3, respectively, using the former scheme [22]. (c-d) S_1⊕S_2 and S_2⊕S_3, respectively, using the proposed method with two different masking coefficients………………………………………………………………………………...83
Fig. 3.1. Example of generated hyperchaos numbers over various types. The first and second lines are with hyperchaos type 1 and 3, respectively. The left and right column are X_n and Y_n, respectively………………………………………………………………………………………….89
Fig. 3.2. Example of hyperchaos image permutation: (a) generated hyperchaos random numbers X_n, (b) sorted hyperchaos numbers, (c) plain image, and (d) scrambled image………………………………………………………………………………..………………93
Fig. 3.3. Example of hyperchaos permutation on plain image (a) using several hyperchaos keys: (b) k=1, (c) k=2, and (d) k=3…………………………………………………………………….94
Fig. 3.4. Comparison between hyperchaos permutation and Arnold transformation over: (first line) grayscale, and (second line) color image. The histogram of each image is given at the bottom-left part. The first to the third columns are the results of Arnold transformation after one, three, and five rounds processing. The last column is the result of hyperchaos image scrambling……..…………………97
Fig. 3.5. Schematic diagram of proposed method for grayscale image: (a) watermark embedding, and (b) watermark extraction………………………………………………………………………100
Fig. 3.6. Schematic diagram of proposed method for color image: (a) watermark embedding, and (b) watermark extraction……..……………………………………………………………………….106
Fig. 3.7. Schematic diagram of FPP watermark embedding on: (a) real owner, and (b) counterfeit attacker side. Watermark extraction on: (c) real owner watermarked image, and (d) arbitrary image, using counterfeit side information…………………………………………………………………110
Fig. 3.8. Test for false positive problem: (a) watermark image W, (b) counterfeit watermark image W_c, (c) watermarked image A_w, (d) counterfeit watermarked image A_wc, (e, g) extracted watermark images W^* from watermarked image A_w and A_wc, respectively, with side information from watermark image W, i.e U_w and V_w, and (f, h) extracted watermark images W_c^* from watermarked image A_w and A_wc, respectively, with side information from watermark W_c, i.e. U_wc and V_wc………………………………………………………………………….…………………....…112
Fig. 3.9. Image sets: (a) Baboon, (b) Lake, (c) Peppers, and (d) Barbara…………………………115
Fig. 3.10. Effect of changing singular value matrix Σ of Lena image using singular value of images given Fig. 3.9……………………………………………………………………………………....117
Fig. 3.11. Effect of principal components UΣ of Lena image using singular value of images given Fig. 3.9……………………………………………………………………………………………….....121
Fig. 3.12. Comparison of MSE values between theoretical and real computation over various scaling factors on: (a-b) DCT, and (c-d) SVD approaches………………………………………………….127
Fig. 3.13. Comparison of PSNR scores over various scaling factors on: (a-b) DCT, and (c-d) SVD
approaches……………………………………………..…………………………………………..129
Fig. 3.14. Imperceptibility Test: (a) host image, (b) watermark image, (c) scrambled watermark image, (d) watermarked image, (e) extracted watermark image in scrambled version, (f) descrambled extracted watermark image with correct key k=1, (g-i) descrambled extracted watermark image with wrong key k=2, k=3, and k=4, respectively…….……………………………………...131
Fig. 3.15. Performance comparison of proposed method in terms of MSE score over: (a) negative, and (b) positive scaling factor………………………………….…………………………………..133
Fig. 3.16. Performance comparison of proposed method in terms of PSNR score over: (a) negative, and (b) positive scaling factor……………………………………………………………………………………………….134
Fig. 3.17. Effect of different scaling factors over various hyperchaos permutation types…………………………………………………………………………………………….….135
Fig. 3.18. Robustness test of watermarked images under several attacks: (a) JPEG compression Q=80, (b) Gaussian noise 0.001, (c) multiplicative uniform noise 0.005, (d) additive uniform noise 0.005, (e) salt and pepper noise 0.01, (f) mean filter, (g) gamma correction, (h) image sharpening, (i) image rescaling 512×512→256×256→512×512, (j) image croping, (k) median filter 3×3, and (l) speckle noise 0.001………………………………………………………………………………...137
Fig. 3.19. Extracted watermark images under several attacks…….……………………………….139
Fig. 3.20. Robustness Test against False-Positive-Free issue: (a) watermark image 1, (b) watermark image 2, (c) watermarked image 1, (d) watermarked image 2, (e-f) extracted watermark 1 and 2 using correct keys, (g) extracted watermark 1 using wrong key from watermark 2, and (h) extracted watermark 2 using wrong key from watermark 1……………….…………………………………140
Fig. 3.21. Imperceptibility test on color image: (a) host image, (b) watermark image, (c) scrambled watermark image, (d) watermarked image, (e) scrambled extracted watermark image, (f) descrambled extracted watermark image using correct chaotic keys, and (g-i) descrambled extracted watermark image using incorrect hyperchaos keys…………………………………………………………….146
Fig. 3.22. Robustness attacks on color image: (a-l) watermarked images under some geometric manipulation and distortion as similarly given in Fig. 3.17……………………….……………….148
Fig. 3.23. Extracted watermark images from attacked watermarked images of Fig. 3.21………….149
Fig.4.1. Error diffusion on image block corner……………………………………………………158
Fig. 4.2. Schematic diagram of CF Construction………………………………………………….161
Fig. 4.3. Illustration of CF Bit-probability, (a) Max and Min Quantizer, (b) Max and Min Quantizer converted into binary……………………………………………………………………………...163
Fig. 4.4. Schematic diagram of BF Construction…………………………………………………165
Fig. 4.5. Illustration of BF Bit-Probability………………………………………………………..166
Fig. 4.6. The flowchart of DDBTC with Bit Probability…………………………………….……172
Fig. 4.7. Image retrieval result from image database Alot-4000……………………………….…178
Fig. 4.8. Image retrieval result from image database Brodatz-640……………………………….178
Fig. 4.9. Image retrieval result from image database Brodatz-1856……………………………...179
Fig. 4.10. Image retrieval result from image database Brodatz-416……………………….……..179
Fig. 4.11. Image retrieval result from image database Stex-7616………………………….……..180
Fig. 4.12. Image retrieval result from image database Vistex-640………………………….…….180
Fig. 4.13. Image retrieval result from image Corel-1000 database……………………………….184













List of Tables
Table 1.1. Dissertation organization…………………………………………………………………5
Table 2.1. Similarity of Adjacent Pixels in Terms of Correlation Coefficient……………………...47
Table 2.2. Performance Comparisons Between the Proposed Image Encryption and Former Schemes in Terms of Average Information Entropy…………………………………………………….…….48
Table 2.3. Performance Comparisons Between the Proposed Image Encryption and Former Schemes in Terms of Differential Attacks……………………………………………………………..………48
Table 2.4. Performance Evaluation of the Proposed Method in Terms of Similarity Between the Secret and Recovered Images…………………………………………………………………….……......73
Table 2.5. Performance Evaluation of the Proposed Method in Terms of Differential Attacks……………………………………………………………………..……………..…………74
Table 2.6. Performance Comparisons Between the Proposed method and Former Schemes in Terms of Quantitative Results over Secret and Shared Images…………………………………….………..76
Table 2.7. Performance Comparisons Between the Proposed method and Former Schemes in Terms of Quantitative Results over Shared Images…………………………………………….…………..79
Table 2.8. Comparison between the Proposed Method and Former Schemes……………………….81
Table 3.1. Hyperchaos Discrete Nonlinear Dynamic Systems………………………………...……90
Table 3.2. Effect of changing singular value matrix Σ in terms of MSE and PSNR values………………………………………………………………………………………………117
Table 3.3. Effect of changing principal components UΣ in terms of MSE and PSNR scores………………………………………………………………………………………………122
Table 3.4. Performance Evaluation of Different Hyperchaos Permutations on FPF-SVD Image Watermarking……………………………………………………………………………………...142
Table 3.5. Performance Comparison between the Proposed Method and Former SVD-Based Image Watermarking Schemes in Terms of Algorithm Aspects…………………………….………….....143
Table 4.1. Class Matrix and The Corresponding Diffused Matrix Used In Our Experiments….…156
Table 4.2. Summary of Image Database Used in Experiment……………………….…………….173
Table 4.3. Image Retrieval Performance in Terms of ARR for Brodatz-1856 and Vistex-640 image databases…………………………………………………………………………………….…….174
Table 4.4. Image Retrieval Performance in Terms of ARR under Brodatz-416 and Brodatz-640 image databases………………………………………………………………………………………….160
Table 4.5. Image Retrieval Performance in Terms of ARR under ALOT image database…….…162
Table 4.6. Image Retrieval Performance in Terms of ARR under Stex-7616 image database…...163
Table 4.7. Image Retrieval Performance in Terms of APR under Brodatz-1856 image database..182
Table 4.8. Image Retrieval Performance in Terms of APR under Vistex-640 image database…...182
Table 4.9. Image Retrieval Performance in Terms of APR under Alot-4000 image database……182
Table 4.10. Image Retrieval Performance in Terms of APR under Brodatz-416 image database..182
Table 4.11. Image Retrieval Performance in Terms of APR under Brodatz-640 image database.183
Table 4.12. Image Retrieval Performance in Terms of APR under Stex-7616 image database….183
Table 4.13. Image classification performance under Vistex 864, USPTex, and Outex TC-00013 image databases…………………………………………………………………………………………171
Table 4.14. Image classification performance under KTH-TIPS image databases……….………...185
Table 4.15. Image classification performance under KTH-TIPS 2A image databases……………..185
Table 4.16. The retrieving time a similar image the computation complexity compared to the former scheme of the 640 image brodatz……………………………………………………….……….....186
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