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研究生:冷輝世
研究生(外文):Hui-Shih Leng
論文名稱:資訊隱藏的研究-使用完全平方數、混合邊緣偵測與最小失真以及模數函數
論文名稱(外文):Data Hiding Using the Perfect Square Number, Hybrid Edge Detector with Minimal Distortion and the Modulus Function
指導教授:曾顯文曾顯文引用關係
指導教授(外文):Hsien-Wen Tseng
口試委員:詹永寬蔡垂雄曾顯文呂慈純洪國龍
口試委員(外文):Yung-Kuan ChanChwei-Shyong TasiHsien-Wen TsengTzu-Chuen LuKuo-Lung Hung
口試日期:2014-07-30
學位類別:博士
校院名稱:朝陽科技大學
系所名稱:資訊管理系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:64
中文關鍵詞:不可逆式資訊隱藏鄰近相素差值藏入法邊緣偵測最低位元藏入法模數函數藏入法
外文關鍵詞:Irreversible data hidingpixel-value-differencinghybrid edge detectorleast-significant-bit substitutionmodulus function
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資訊隱藏是一門將資訊藏入載體並秘密傳送的技術。由於數位影像被廣泛的使用在網際網路上,且具有藏量大的特性,因此常被用來做為載體。當將機密訊息嵌入原始影像後產生偽裝影像,且此偽裝影像相較於原始影像會有失真,如何減少失真則是資訊隱藏的重要課題。資訊隱藏的評估有兩項重要的指標,藏入量與隱蔽性。藏入量是指由原始影像中平均每個像素所嵌入的機密訊息數量所決定,隱蔽性則是由計算PSNR值來評估。高隱蔽性代表原始影像與偽裝影像的差異小,亦即低失真。
資訊隱藏依其特性又分為可逆式與不可逆式兩種。可逆式是指接收方在擷取出機密訊息後可以無失真的還原原始影像。由於必須保留部份額外資訊以還原原始影像,所以藏入量比一般不可逆式低。雖然不可逆式藏量較大,但是必須符合人眼無法辨識的程度。
本論文提出三種常見的不可逆式資訊隱藏技術提出改進的方法。首先,在鄰近像素差值藏入法的量化表中使用完全平方數取代傳統的二進位值做為量化表的範圍。其次,在邊緣偵測和最低位元藏入法中使用新的邊緣檢測方式增加藏入量並以最小失真法提高偽裝影像的品質。最後,對模數函數藏入法引入新的概念以及延伸應用。

Data hiding is a technique that conceals data into a carrier for conveying the secret message confidentially. Digital images are widely transmitted over the internet and with large payload, so digital images often serve as a carrier. After embedding the secret message into the cover image, the cover image termed as stego image and distortion occur. Reduce the distortion is an important issue in data hiding. The measurement of data hiding has two requirements, payload and imperceptibility. The payload is determined by the number of secret message embedded in each pixel on the cover image. The imperceptibility is calculated by peak signal-to-noise ratio. High imperceptibility implies low distortion difference between cover image and stego image.
Data hiding has two types: reversible and irreversible. The reversible data hiding can restore the cover image without any distortion after the secret message has been extracted. Due to requirement of extra information of reversible data hiding, it has lower payload than most irreversible data hiding schemes. Despite the irreversible data hiding has a higher payload; it must comply with the human visual system which implies the stego image cannot be recognized by human eyes.
In this dissertation, we propose three methods of irreversible data hiding. First, we use the perfect square number divide the quantization range table on the pixel-value-differencing scheme. Second, we use a new hybrid edge detector increase the embedding capacity by least-significant-bit substitution scheme and use the minimal distortion method to improve the quality of the stego image. Finally, we try to use the geometry relation on the modulus function to replace the calculation complexity and extend the exploiting-modification-direction scheme and the fully-exploiting-modification-direction scheme to n-dimensional hypercube.

Chapter 1 Introduction 1
1.1 Research Motivation 1
1.2 Objectives and Research Scopes 2
1.3 Organization 3
Chapter 2 Related Works 4
2.1 The PVD scheme 4
2.2 The edge detector combines with the LSB substitution scheme 5
2.2.1 The LSB substitution scheme 6
2.2.2 The Optimal-Pixel-Adjustment-Process scheme 6
2.2.3 Fuzzy edge detector 7
2.2.4 Chen et al.'s scheme 9
2.3 The modulus function scheme 11
2.3.1 The EMD scheme 12
2.3.2 The FEMD scheme 13
2.4 Test images and the measurement of experimental results 16
Chapter 3 Data Hiding Using the Perfect Square Number 18
3.1 Using the perfect square number design the new quantization range table 18
3.2 Embedding phase 21
3.3 Extraction phase 26
3.4 Theoretical analysis and experimental results 28
3.4.1 Theoretical analysis 28
3.4.2 Experimental results 31
3.5 Summaries 31
Chapter 4 Data Hiding Using Hybrid Edge Detector with Minimal Distortion 32
4.1 Embedding phase 33
4.2 Extraction phase 37
4.3 Experimental results 38
4.4 Summaries 43
Chapter 5 Data Hiding Using the Modulus Function 44
5.1 Using 3-Axis in 2D 44
5.1.1 Embedding phase 44
5.1.2 Extraction phase 46
5.1.3 Customize the extracting function 47
5.1.4 Experimental results 48
5.2 Using n-dimensional hypercube 48
5.2.1 Embedding and extraction phase 49
5.2.2 Minimal distortion process 54
5.2.3 Generalization 55
5.2.4 Experimental results 56
5.3 Summaries 58
Chapter 6 Conclusions and Future Works 59
6.1 Conclusions 59
6.2 Future works 59
Bibliography 61

List of Tables
Table 2.1 The fuzzy rule matrix 9
Table 3.1 The quantization range table based on the perfect square number (n is
the perfect square number, m is the length of embedding bits) 20
Table 3.2 Distributions of pixel-value difference, average payload and average
MSE for images using the proposed method 29
Table 3.3 The distributions of pixel-value difference, payload and MSE for
images using Wu and Tsai’s method 30
Table 3.4 Comparison between Wu and Tsai’s method and the proposed method
by theoretical analysis 30
Table 3.5 The experiment results with real test 31
Table 4.1 Comparison between Chen et al.'s hybrid edge detector and our hybrid
edge detector 32
Table 4.2 The MSE in Case 1-4 with secret data bit stream
'001110110011000011111010' 36
Table 4.3 Comparison with Chen et al.'s result when x = 3 and x = 4 for the
'Lena' image sized 128*128 39
Table 4.4 Experiment results of the proposed method using various x values on
six test images sized 128*128 39
Table 5.1 Four kinds of the F value labeled in the center of Eq. (5.1) 45
Table 5.2 Embedding procedure for each three-pixel block (x1,x2,x3) 45
Table 5.3 Embedding procedure for each three-pixel block (x1,x2,x3) of Eq. (5.2) 47
Table 5.4 Experimental results when the proposed method using random secret
digits on test images 48
Table 5.5 The one-to-one mapping relation when w = 3 50
Table 5.6 The one-to-one mapping relation when w = 5 52
Table 5.7 The experimental results of embedding 27-ary secret digits (n = 3, w =
3) 56
Table 5.8 The experimental results of embedding 125-ary secret digits (n = 3, w
= 5) 56

List of Figures
Fig. 2.1 22 mask used for scanning 8
Fig. 2.2 Chen et al.'s embedding example for n = 5, x = 1 and y = 3. 10
Fig. 2.3 Chen et al.'s extraction example for n = 5, x = 1 and y = 3. 11
Fig. 2.4 Hypercube of the EMD scheme (n = 2) 13
Fig. 2.5 Hypercube of the FEMD scheme when (a) k = 3 and (b) k = 4 15
Fig. 2.6 Six test images: (a)Tiffany (b)Baboon (c)Lena (d)Jet (e)Lake and
(f)Peppers. 16
Fig. 3.1 Divide each range by the perfect square number 19
Fig. 3.2 The embedding procedure. 23
Fig. 3.3 The embedding examples of two consecutive pixels (47,81) and two
cases of secret data ‘0000’ and ‘001’. 25
Fig. 3.4 The extraction procedure. 27
Fig. 4.1 Example of the proposed method 34
Fig. 4.2 Flowchart of the extraction procedure 38
Fig. 5.1 3-Axis in 2D 44
Fig. 5.2 Four kinds of the F value labeled in the center of Eq. (5.2) 47
Fig. 5.3 (g1,g2,g3) as the center of a cube in 3D space 49
Fig. 5.4 Minimum distortion process of the proposed method 54
Fig. 5.5 Comparisons of the EMD scheme (n = 2, 3, 4, 5), the FEMD scheme (k
= 2, 3, 4, 6, 8), and the proposed scheme (n = 3; w = 3, 5, 7, 9). 57
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