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研究生:孔孟維
研究生(外文):Meng-Wei Kong
論文名稱:基於隨機網格技術的強韌型存取結構的視覺密碼之研究
論文名稱(外文):A study of strong general access structure based on random grids visual secret sharing
指導教授:吳憲珠
指導教授(外文):Xian-Zhu Wu
學位類別:碩士
校院名稱:國立臺中科技大學
系所名稱:資訊工程系碩士班
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:2017
畢業學年度:105
語文別:中文
論文頁數:30
中文關鍵詞:視覺密碼一般型存取結構影像分享門檻值陰影圖像資訊安全
外文關鍵詞:Visual cryptographystrong general access structuresecret sharingThresholdshadow imageinformation security
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傳統的影像密碼學,可大致分為視覺加密(Visual Cryptography, VC)及隨機網格(Random Grid, RG)兩種。視覺加密是一種依照編碼表,將秘密影像拆分成多張分享影像(shares),透過疊合這些分享影像達到一定的門檻值,可以還原成當初的秘密影像,數量不足則不會顯示,我們稱之為(k,n)VCS。但通常會有著編碼表的需求,且需要將影像擴展為M倍來進行藏入,許多研究也在探討這方面的問題。而RG則是另外一種技術,它並不需要將影像擴展為M倍,且也不需要編碼表。也就是說,還原影像與秘密影像得大小是一樣大的並且也不需要記憶複雜的密碼表,只要依靠簡單的操作就可以完成藏密的動作。但取而代之的是,它犧牲了還原影像的對比度。
在本篇提出的方法中,使用了在VC中的一種特殊型的結構,名叫一般型存取結構(general access structure ,GAS),它是一種由使用者指定組合,並且唯有特定組合才可以疊合的變體(k,n)VCS。相較於傳統的(k,n)VCS,運用這樣子的結構有著較為自由的優點,可以由設計者決定由那些使用者在怎樣的情況下可疊回來。但傳統上的GAS,卻有著疊合回來後影像品質較差,需要倚賴編碼表以及影像擴張等等的缺點在。因此在本篇提出的方法中,我們改善了這些缺陷,並不需要使用編碼表去進行藏入的動作,並且分享影像的大小,與秘密影像的大小是相等的,而並不需要進行擴張,並且,還原影像的對比度也較之前的方法來的高。
如今,隨著影像密碼的發展越來越成熟,看見混濁的分享影像,竊取者也可能會查覺到這樣的一張陰影圖像是分享影像,且其中可能藏著資訊。因此,發展出了新的技術,讓原本分享影像並非是陰影圖像而是有意義的圖像來隱藏真正想要傳達的資訊。在第二個方法中,本篇方法也提出了一個多張有意義影像的做法,且還原的影像是清晰可見的
In the tradition, image cryptography can be roughly divided into two kinds: Visual Cryptography (VC) and Random Grid (RG). Visual Cryptography is according to the code table, divided the secret image into multiple shares, and if superimposing these shared to reach a certain threshold, can be clear see to the original secret image, but the number is short can’t displayed, this is called (k, n) VCS. It usually needs a coding table, and needs to expand the image M times to be hidden, therefore have many studies are exploring this issue. The RG is another technique, it does not need to expand the image to M times, and do not need the coding table. That is to say, the restored image is as large as the secret image size and does not need to memorize a complicated password table, so long as the operation can be performed by simple operation. But instead it sacrifices the contrast of the restored image.
In the proposed method, a special type of structure called general access structure (GAS) is used in VC, which is a user-specified combination, and can only be superimposed by a specific combination Variables (k, n) VCS. Compared with the traditional (k, n) VCS, this structure has the advantage of freedom, for the designer can decide by whom or in which circumstances it can be stacked back. However, the traditional GAS has defects like poor image quality after stacking and having to rely on coding table and image expansion. Therefore, in the proposed method, we improve these defects. It does not need to use the code table to carry out the action of embedding, and the sizes of the share image and the secret image are equal, needless to expand. Also, the contrast of the restored image is higher than that of the previous method.
Nowadays, as Visual Cryptography grow more sophisticated and are able to see haze-sharing images, stealers may also perceive such a shadow image as a shared image, containing information. As a result, new technologies have been developed to share images which are not shadow images but meaningful images to hide the information that is really intended to be conveyed. In the second method, it also presents the way to make a number of meaningful images, and the recovery image is clearly visible
ABSTRACT in Chinese i
ABSTRACT in English iii
Table of Contents v
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Background 2
1.3 Thesis organization 5
Chapter 2 Related works 7
2.1 (K, K) RG-BASED VCS 7
2.2 The (k, n) RG-based Visual Cryptography Method 9
2.3 The Strong General Access Structure of Visual Secret sharing(GAS) 10
Chapter 3 Proposed method 13
3.1 The design of proposed method 13
3.2 Proposed scheme algorithm 16
3.3 The Extended Proposed Methods with Friendly Algorithm 19
Chapter 4 Experimental results 23
4.1 The Result of the Proposed Scheme 23
4.2 The Results of the Proposed Friendly Scheme 25
Chapter 5 Conclusions and Future works 28
5.1 Conclusions 28
5.2 Future works 28
References 29
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