臺灣博碩士論文加值系統

本論文主要探索數位影像的保護技術用以保護數位影像的智慧財產權和解決影像的竄改偵測和復原的問題。
首先, 本論文提出六個數位影像的保護技術。第一個保護技術利用視覺密碼技術為每一個著作權從被保護的原始數位影像建造出一個主分享影像和一個所有權分享影像。當把這二份分享影像相疊合在一起以後，利用人類視覺的系統且不需要任何的計算，便能夠直接顯示出所有權的資訊。我們的方法不改變原始數位影像而且也能決定一個原始數位影像的對應多個所有權。此外，我們的方法具有高安全性，對於有意侵犯著作權者不能偵測出數位影像的所有權資訊和偽造所有權。我們的實驗結果顯示在JPEG的有損耗壓縮、模糊化、加入雜點和裁剪的攻擊後，我們的方法仍然能夠強韌地從原始數位影像中擷取出所有權訊息。
第二個保護技術引用一個規則來結合原始數位影像和數位著作權影像之間的資訊，其中原始數位影像和數位所有權影像二者均是灰階影像。原始灰階影像和灰階著作權影像產生附時間郵戳的著作權鑰匙，這是一把保護原始影像之著作權完整的鑰匙。在著作權鑰匙產生的過程中，區塊切斷技術(BTC)被運用來得到原始影像的區塊特徵值。本技術滿足一個成熟的影像著作權保護技術的所有要求；即它具有能夠以隱形、安全、強韌和多著作權的鑄造能力把數位著作權影像和原始數位影像鑄造成一把著作權鑰匙。實驗結果顯示本技術可以成功使原始影像免於有損耗壓縮、旋轉、銳化、模糊化和裁剪的攻擊，因為從攻擊後的原始影像中仍然可以強韌地擷取出著作權訊息。
第三個提出的保護技術利用四元樹(quadtree)來記錄被保護的原始影像和對應的浮水印影像的資訊。四元樹能夠成功地幫助完成浮水印的鑄造處理和浮水印的驗證處理。除此之外, 從實驗結果顯示本方法能夠從被經過各種攻擊後的原始影像中擷取強韌且可以認知的數位浮水印。
第四個提出的智慧財產權保護技術滿足現代保護技術的需求。針對原始影像，其著作權資訊和影像的離散餘弦轉換(DCT)頻率係數之間的關係能夠以沒有修改原始影像的方式有效地完成著作權的鑄造和偵測。此外，能夠在沒有妨礙彼此的現象下管理原始影像的多個著作權資訊。透過各種影像攻擊的實驗顯示本方法真正具有強韌的特性。
第五個保護技術提供了保護彩色影像的智慧財產權的方法。這個方法引用一個規則透過影像壓縮技術和基因演算法將被保護的彩色原始影像和數位著作權之間的資訊加以結合，然後產生附時間郵戳的著作權鑰匙。當版權爭論發生時，附時間郵戳的著作權鑰匙是證物。本方法能夠提供安全的、隱形的和多著作權的特性以沒有修改被保護的彩色原始影像的方式成功地完成著作權鑄造和驗證。由實驗結果顯示本方法能夠從被各種攻擊後的原始影像中強韌地重建出可以辨識的著作權影像。
　　本論文提出一個以部分編碼法(Fractal coding)為基礎的浮水印技術，有效地保護數位影像的智慧財產權。部分編碼法的主要特性是它在影像的較大和較小的一些區塊之間使用自相似(self-similarity)的特性來壓縮影像。本方法使用這個自相似的關係時，它都能有效地用來完成浮水印的嵌入和擷取。從實驗結果顯示，本方法提供了比其他以部分編碼法壓縮為基礎的浮水印技術更強韌的能力。
另一方面, 研究數位影像的竄改和恢復影像是一個重要的課題，本論文提出兩個技術解決這個問題。首先，將特定的DCT頻率係數當作有價值的影像特徵值，並將它們嵌入到影像像素的最不重要的位元裡，成為影像完整性的驗證的證據。如果影像被竄改，則受影響的嵌入的特徵值照樣會被改變和偵測出來。然後，本技術的復原方法能夠獲得相對的原始特徵值，並用以重建影像。按照實驗的結果，本技術能夠有效地完成確定影像中的竄改點位置以及恢復影像的兩項工作。
第二個提出的技術是將影像的竄改和恢復的能力加入JPEG中，本方法能夠偵測被竄改的影像以及能夠恢復這個影像。在JPEG壓縮技術壓縮影像的期間，本方法結合影像邊緣偵測技術。首先，在圖像壓縮完成以前，邊緣偵測技術先辨識出影像的邊緣。然後，取得的邊緣特徵被嵌入到JPEG壓縮以後的影像裡。如果影像在傳輸期間被竄改，則能夠使用這些嵌入的邊緣特徵值偵測出被竄改的範圍，並且利用內插法和嵌入的邊緣特徵值來重建這些被竄改的部分。因此，本方法能夠防止接收者不知情影像在傳輸期間被竄改的狀況。

The goal of this dissertation is to develop improved image protection techniques to protect the intellectual property rights and to solve the above problems such as tamper proofing and recovery.
First of all, this dissertation presents six digital copyrights protection schemes. The first proposed copyrights protection scheme takes advantage of visual cryptography to construct a master share from a digital image and come with an ownership share for each copyright. After stacking these two shares, the ownership information can be recovered directly by human visual system without any computation. Our method will not change the host image and can be invisible and multiple ownerships determining as well. Besides, our method has high security that piracies and attackers cannot detect ownership information and fake ownership of image. Our experimental results show that after JPEG lossy compression, blurring, noise adding, and cropping attacks, the ownership can still be robustly detected from the host image by our method.
The second proposed copyright protection scheme conducts a rule to combine the related information between the digital host image and digital copyright image, both are gray-scaled images. Gray-scaled host image, and gray-scaled copyright image will be mapped to produce a time-stamped copyright key, a key to make host image intact in copyright protection. In mapping process, block truncation coding (BTC) technique is applied to retrieve a block character value from the host image. The proposed scheme satisfies all of the requirements of a mature image copyright protection technique, that is, it can cast the digital copyright into a host image with the abilities of invisibility, security, robustness and multiple casting. Experiments are successful for host image to survive the attacks of lossy compression, rotating, sharpening, blurring, and cropping, because from the attacked host image, the copyright still remains robust for detection.
The third proposed copyright protection methodology uses a quadtree to record the information of a protected host image and the related watermark. The quadtree can help the processing of watermark casting and watermark verification successfully. Besides that, from the experimental results, we show that the proposed scheme can robustly recover recognizable watermarks from variety of attacked host images.
The fourth intellectual property rights protection scheme is proposed to satisfy modern requirements of protecting techniques. For a host image, the relationship between the copyright information and discrete cosine transformation (DCT) frequency coefficients can effectively cast and detect the copyright without modifying the host image. Besides, multiple copyrights information can be managed without the phenomenon of interfering with each other. The proposed scheme truly possesses robust characteristic through experiments of various image attacks.
The fifth protection scheme is presented to protect the intellectual property rights of color images. This methodology coalesces image compression technique and genetic algorithm for conducting a rule to combine the related information between protected host image and digital copyright image and then generates a time-stamped copyright key. The time-stamped copyright key is a witness when copyright dispute is occurred. The proposed scheme can provide secure, invisible, and multiple casting properties to successfully cast and verify copyright without modifying the host image. As the experiments come out, the proposed scheme can robustly reconstruct recognizable copyright images from variety of modified host images.
This dissertation proposes a fractal-based watermarking scheme that efficiently protects the intellectual property rights of digital images. The main feature of fractal encoding is that it uses the self-similarity between the larger and smaller parts of an image to compress the image. When our scheme uses this self-similar relationship, it effects both the embedding and extraction of the watermark. As seen from the experimental results, the proposed scheme provides more robust capability than other compression-based watermarking techniques.
On the other hand, it is an important issue to detect tamper of digital images and recover the images. We propose two schemes to solve this problem in this dissertation. First, with the specific DCT frequency coefficients taken as the characteristic values, which are embedded into the least significant bits of the image pixels, it is feasible to proof the image integrity. If the image is tampered, the embedded characteristic values that are affected will be changed accordingly and detected. Then the corresponding original characteristic values can be acquired by the proposed recovery process to reconstruct the image. In terms of the results from the experiments, the proposed scheme works absolutely both on identifying the tampered spots in the image as well as on restoring the image.
The second proposed tamper proof and recovery scheme puts forward a technique in JPEG still image compression standard. The proposed scheme can detect tampered image and also can recover the image. During the image compressed by JPEG compression technique, the proposed scheme incorporates with the edge detection technique. First, the edge detection technique will recognize the edges of the image before image compression takes place. The obtained edge characteristic is then embedded into the image right after compression. If the image is tampered during transmission, the embedded edge characteristic can be used for detecting the tampered areas, and these tampered areas may be reconstructed by interpolation method and embedded edges. Therefore, the proposed scheme prevents receivers from being unaware of the tampered image during transmission.

Abstract in Chinese I
Abstract in English III
Acknowledgements VII
Contents VIII
List of Tables XI
List of Figures XII
Chapter 1 Introduction 1-1
1.1 Background 1-1
1.2 Image Copyright protection 1-2
1.3 Tampering Proof and Recovery 1-7
Chapter 2 Preliminaries 2-1
2.1 2 out of 2 Visual Secret Sharing Scheme 2-1
2.2 Block Truncation Coding Technique 2-3
2.3 Quadtree 2-3
2.4 GA-AMBTC 2-5
2.5 Edge Detection Technique 2-9
2.6 JPEG Baseline Sequential Coding 2-11
2.6.1 Color Space Transformation 2-11
2.6.2 Sampling 2-12
2.6.3 Discrete Cosine Transformation 2-12
2.6.4 Quantization 2-13
2.6.5 Entropy Encoding 2-15
Chapter 3 A Copyright Protection Scheme of Images Based on Visual Cryptography 3-1
3.1 Multiple copyright Casting Algorithm 3-2
3.2 Identification of Copyright 3-7
3.3 Experimental Results and Discussions 3-8
Chapter 4 A Secure and Robust Digital Image Copyright Protection Scheme 4-1
4.1 Previous works 4-1
4.2 The Proposed Image Copyright Protection Scheme 4-3
4.2.1 Casting Copyright Process 4-3
4.2.2 Verifying Copyright Process 4-6
4.3 Experimental Results and Discussions 4-7
Chapter 5 Computing Watermarks from Images Using Quadtrees 5-1
5.1 The Proposed Scheme 5-1
5.1.1 Watermark casting process 5-1
5.1.2 Watermark Verification Process 5-5
5.2 Experimental Results and Discussions 5-7
Chapter 6 An Image Protection Scheme Based on Discrete Cosine Transformation 6-1
6.1 DCT-based Copyright Protection Scheme 6-1
6.1.1 Copyright Casting 6-1
6.1.2 Watermark Detection 6-4
6.2 Experimental Results and Discussion 6-6
Chapter 7 Using a Genetic Algorithm to Safeguard the Intellectual Property Rights of Color Images 7-1
7.1 The Proposed Copyright Protection Scheme 7-1
7.1.1 Copyright Casting Process 7-1
7.1.2 Copyright Verifying Process 7-4
7.2 Experimental Results and Discussion 7-6
Chapter 8 Hiding Digital Watermarks Using Fractal Compression Technique 8-1
8.1 Fractal Image Compression Technique 8-1
8.2 The Proposed Scheme 8-3
8.2.1 Watermark Embedding Process 8-3
8.2.2 Watermark Extraction Process 8-5
8.3 ExperimentalResults 8-6
Chapter 9 A DCT-based Recoverable Image Tampering Proof Technique 9-1
9.1 Embedding Process 9-1
9.1.1 Image Modifying 9-2
9.1.2 Discrete Cosine Transformation 9-2
9.1.3 Acquirement of Characteristic Values 9-2
9.1.4 Inserting Verification Data into Each Characteristic Value 9-3
9.1.5 Replication of Characteristic Values 9-4
9.1.6 Encryption of Characteristic Values 9-5
9.1.7 Characteristic Values Embedding 9-5
9.2 Tampering Detecting and Image Restoring Processes 9-7
9.2.1 Acquirement of Embedded Characteristic Values from Image 9-8
9.2.2 Decryption of Extracted Embedded Characteristic Values 9-8
9.2.3 Acquirement of Characteristic Values from Stego-image 9-9
9.2.4 Matching Characteristic Values (Integrity of Image Proof) 9-9
9.2.5 Picking a Right Set of Characteristic Values 9-10
9.2.6 Inverse Discrete Cosine Transformation for the Recovering Block 9-10
9.3 Experimental Results and Discussion 9-13
Chapter 10 Detection and Restoration of Tampered JPEG Compressed Images 10-1
10.1 Embedding Process 10-1
10.1.1 Image Normalizing 10-2
10.1.1.1 Normalization for Image Compression 10-2
10.1.1.2 Normalization for Embedded Characteristic Values 10-3
10.1.2 Acquiring Characteristic Values 10-4
10.1.3 Image Compression and Embedding Characteristic Values 10-5
10.2 Tampered Detecting and Recovering Process 10-5
10.2.1 Acquiring Embedded Characteristic Values from Image 10-7
10.2.2 Composing a Right Set of Embedded Characteristic Values 10-7
10.2.3 Normalization of Stego-image 10-7
10.2.4 Acquiring Characteristic Values from Stego-image 10-8
10.2.5 Matching Characteristic Values (Integrity of Image Proof) 10-8
10.2.6 Decompressing the Stego-image 10-9
10.2.7 Recovering the Tampered Blocks 10-9
10.3 Experimental Results 10-10
Chapter 11 Conclusions and Future Researches 11-1
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