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Data Hiding
• 在二次大戰時期，德國間諜就曾用下列文章傳遞訊息。
Apparently neutral's protest is thoroughly discounted and ignored. Isman hard hit. Blockade issue affects pretext for embargo on byproducts ，
ejecting suet and vegetable oils.• 將上述文章中每個字的第二個字母抽取出來組合，即透露出以下的訊 :
Pershing sails from NY June 1.
學術研究領域分類
• 數位浮水印 /數位資料隱藏• 數位浮水印—可見 /不可見• 數位浮水印—強固 /易碎• 數位浮水印— watermark extraction/detection• 數位資料隱藏— for harm/for good• 數位資料隱藏— spatial domain/frequency domain
• 數位資料隱藏— reversible/not reversible
資訊隱藏技術之分類• 資料隱藏 ( 破壞性或非破壞性 )
–保護藏入媒體之機密資料 -- 資料隱藏法 (steganography) 藏入資料量多
• 數位浮水印 ( 破壞性 )– 保護原始媒體 -- 數位浮水印 (digital
watermarking) 藏入資料量少
數位浮水印 -- 可見 /不可見I n tr odu ction
V isible I nvisible
資訊隱藏的應用－專利文件資料保護( 易碎型浮水印 )
資訊隱藏的應用強固型浮水印
Dual-wrapped digital watermarking scheme for image copyright protection Computers & Security Volume: 26, Issue: 4, June, 2007, pp. 319-330 Hu, Ming-Chiang; Lou, Der-Chyuan; Chang, Ming-Chang
Watermark detection
(a): The original image of test object “Russian Doll”. (b): The normalized form using the proposed method. (c): Shape self-similar segmentation using the proposed method. (d):The corresponding detector response using the proposed method. (e):The corresponding detector response using the method of Lu.
(a)
(b) (c)
(d) (e)
Properties of Information hiding
Undetectability Robustness
Capacity
The “Magic” Triangle
There is a trade-offbetween capacity,invisibility, and robustness
Secure steganographictechniques
Digital watermarking
• Complexity of embedding / extraction• Security
Additional factors:
Naïve steganography
常用度量值• PSNR(Peak Signal Noise Ratio ) is used to measure the gray
image quality
• NC (Normalized Correlation) is used to measure the similarity between two bi-level watermarks
where wi and wi’ are the original and extracted watermar
ks
ii
iii
ii
iii
w
ww
w
wwNC
22 )1(
)'1()1('
資料隱藏的應用－遺失區塊重建
資料隱藏的應用－遺失區塊重建
資料隱藏的類型 I
•非破壞性 ( 原始寄主媒體可以被還原 )– 附加在檔案格式之後 (一般檔案型病毒原理 )
實例： DOS 指令Copy file1.doc/b + file2.doc/b file3.doc
原影像圖檔
原影像圖檔
秘密資料秘密資料
偽裝影像圖檔
偽裝影像圖檔
Append
非破壞性資料隱藏的實例– 化整為零藏入檔案格式中
MS-WORD 97 檔案格式A word doc file consists of a main stream, a summary information stream, a table stream, a data stream, and 0 or more object streams which contain private data for OLE 2.0 objects embedded within the Word document.
office 97 一個空白 word 檔案所佔空間 19456 bytes
office XP 一個空白 word 檔案所佔空間 24064 bytes
Word Dumper is a very interesting utility that prints out, among other information, the document revision log. What follows is the (sanitized) revision log of a document found on the Internet obtained using Word Dumper:
• Rev. #1: "Prof. John Brown" edited file “C:\Doc2\Documents\Newb.doc"• Rev. #2: "Prof. John Brown" edited file "C:\Doc2\Documents\Newb.doc"• Rev. #3: "Prof. John Brown" edited file ‘‘C:\Doc2\Documents\Newb.doc"• Rev. #4: "Prof. John Brown" edited file "C:\Doc2\Documents\Newb.doc"• Rev. #5: "Prof. John Brown" edited file "C:\Doc2\Documents\Newb.doc"• Rev. #6: "ACHEP" edited file “\\Doe\C\Documents\xyz Group\Newb. doc"• Rev. #7: "Mike Doe" edited file "C:\Documents\Newb.doc"
• Rev. #8: "Mike Doe" edited file "C:\WINDOWS\Application Data \Microsoft\Word\AutoRecoverysaveofNewbasd"• Rev. #9: "Mike Doe" edited file "C:\WINDOWS\Application Data \Microsoft\Word\AutoRecoverysaveofNewbasd"• Rev. #10: "makeinf" edited file "C:\esprit_2000.doc“ It seems that at least four people edited the
document. Its original file name was Newb.doc and probably John Brown created it. After several modifications, the dcument was copied on Mike Doe’s computer and ACHEP edited it from a remote host. It is possible that ACHEP moved the document from Brown’s computer to Doe’s one. Also Doe modified the document and probably his computer shut down improperly, as we can see from the AutoRecovery save. Finally, makeinf made last changes and gave to the document its final name.
CIH 電腦病毒 「 CIH 」，俗稱「 32位元 CIH 電腦病毒」，此一病毒，利用肝腸寸斷的隱藏技術隱匿在多種應用廣泛的程式中，可逃過多種防毒軟體的耳目，使用者若不有效更新病毒碼及掃瞄程式，也極易潛伏 CIH 病毒而不自知。 CIH 電腦腸病毒使用高難度技術，更狡猾的是，被病毒感染的長度並不會增加，而且沒有任何發作畫面，讓一般人無法察覺此病毒何時開始駐存在你的主記憶體中。 CIH 電腦腸病毒尤其針對 Windows 98 之 32 位元執行檔，利用常駐在記憶體的機會，達到大量感染的目的。所以只要執行任一個中毒檔案後，即會再傳染給其他檔案，也就是說只要有一個檔案未清除，下一個檔案又會骨牌式的全面一一感染。
• 了解常用數位多媒體檔案格式用以尋找合適藏資料的寄主檔案
名稱 說明 BITMAPFILEHEADER bmpfh(14 Bytes) 檔案檔頭部分 BITMAPINFOHEADER bmpih(40 Bytes) 資料檔頭部分 RGBQUAD aColors[]; 用來描述使用的顏色 BYTE aBitmapBits[] 實際影像資料
位移量 形 態 名 稱 定 義 內容
0~1 UINT BfType BMP識別字符 42 4D => BM
2~5 DWORD
BfSize 檔案總長度38 75 02 00 => 161,080 Bytes
6~7 UINT Bfreserved1
保留保留 8~9 UINT Bfreserved
2保留保留
10~13 DWORD Bfoffbits 實際影像資料位置
36 04 00 00 => 1078
BMP 圖形檔案格式
•破壞性 ( 原始寄主媒體無法被還原 )– 利用人類的視覺與聽覺辨識的靈敏度不高 修改數位媒體“較不會被感覺到”的部分資料， 企圖暗渡陳倉– 大多數的研究性質文獻多屬此類– 主要的資料隱藏方式
資料隱藏的類型 II
數位浮水印•數位浮水印是為了保護原著作者的權利，避免使用者有意無意中去觸犯到智慧財產權擁有者的權利，在數位圖檔裡頭加入宣告擁有者 (owner) 資訊的一種技術。數位浮水印又可分為顯性浮水印與隱性浮水印兩種，前者即是可以在圖檔上明顯看出智慧財產權宣告的資訊；後者則是為了不破壞圖檔畫面為原則，將智慧財產權宣告的資訊隱藏在圖檔中。
數位浮水印應用 -智財權保護與竄改偵測
混合網點之數位浮水印技術• 在同一份文件當中，其印紋區塊採用不同的網點形式噴印，
達成視覺上濃度一致的效果，原稿一但經過複印，預先藏匿的浮水印將在複製稿上明顯浮現，此技術作用在於保護紋安全性，避免非法複印的情形發生。
加入浮水印後並印出的底紋圖像，在一定距離觀察人眼無法感知有任何的灰度差異。
經複印機複印之後，浮水印潛像即浮現。
利用掃描器在 600dpi 的解析度下掃描所得之影像，因為取樣不足可清楚偵測浮水印。
「實體影像之數位浮水印技術」計畫主持人：臺灣師範大學科技學院圖文傳播學系 副教授 王希俊共同主持人：中央研究院資訊科學研究所 助研究員 呂俊賢
混合網點之數位浮水印技術 (續 )
(b)
「實體影像之數位浮水印技術」計畫主持人：臺灣師範大學科技學院圖文傳播學系 副教授 王希俊共同主持人：中央研究院資訊科學研究所 助研究員 呂俊賢
(a) (c) (d)
系統設計與需求系統設計與需求1. 隱蔽性(Imperceptibility, Transparency)：
嵌入資訊後的媒體與原媒體的品質差異程度要低。
2. 不可偵測性(Undetectability)：嵌入的資訊必須是電腦所無法偵測分析出來的。
3. 強韌性(Robustness)：嵌入資訊後的掩護媒體接受攻擊的能力。
4. 安全性(Security)： 系統的安全性建築在代表嵌入位置的嵌入金匙上。
5. 隱藏量(Capacity)：掩護媒體所能嵌入的最大資訊量。
6. 明確性(Unambiguity)：取出的嵌入資訊必須能夠明確地證明其合法擁有者。
7. 不需原始媒體（decodability）：不必經由原始媒體的幫助即可取出嵌入資訊。
1. 隱蔽性(Imperceptibility, Transparency)：嵌入資訊後的媒體與原媒體的品質差異程度要低。
2. 不可偵測性(Undetectability)：嵌入的資訊必須是電腦所無法偵測分析出來的。
3. 強韌性(Robustness)：嵌入資訊後的掩護媒體接受攻擊的能力。
4. 安全性(Security)： 系統的安全性建築在代表嵌入位置的嵌入金匙上。
5. 隱藏量(Capacity)：掩護媒體所能嵌入的最大資訊量。
6. 明確性(Unambiguity)：取出的嵌入資訊必須能夠明確地證明其合法擁有者。
7. 不需原始媒體（decodability）：不必經由原始媒體的幫助即可取出嵌入資訊。
系統設計技術面系統設計技術面• 選定數位媒體－影像、視訊、文件、…• 3W－What Where How
– What 要藏什麼資料進去？– Where 要藏在哪裡？– How 如何藏 (operation)？
Document Watermarking
• Line-shifts( 行距 )
• Word-shifts( 字距 )
Document Watermarking
全彩影像 256彩色影像 256灰階影像
黑白影像 (thresholding)
黑白影像 (error diffusion)
黑白影像 (order dithering)
常見數位影像類別
256 色 64 色
LSB 實例說明
16 色 8 色
LSB 實例說明
• 256色灰階影像的結構
影像資料隱藏的技術 LSB
1 0 1 1 0 0 1 1Main significant bit
(MSB)Least
significantbit
(LSB)
2-LSBs data extracting
Extracting procedure
Secret= (11 00 01 00)2
103
(01100111)2
100
(01100100)2
101
(01100101)2
100
(01100100)2
Assume k = 2
利用影像壓縮之資料隱藏的模式
一般圖像(256 x 256 pixels)
偽裝圖(256 x 256 pixels)機密圖
(512 x 512 pixels)
藏入 得到
偽裝圖(256 x 256 pixels) 機密圖
(512 x 512 pixels)
萃取
Palette Image Steganographic Techniques
A palette image of a pocket monster and its corresponding palette.
Palette Image Steganographic Techniques
• Steganographers tend not to like to use palette-based images, because the limitation on the colors available in a finite palette causes difficulties in hiding data.
• Two approaches to embedding messages in palette-based images have been described; they include embedding messages into the palette and embedding messages into image data.
Palette Image Steganographic Techniques• The main advantage of the first approach is that
is easy to implement. – Gifshuffle (Permuting palette entries)
• Image is not modified• Very limited capacity of log2(256!)=215 bytes• Too fragile (resaving)• Suspicious palette order is an artifact
– LSB encoding in the palette• Very limited capacity (at most 3256 bits)• Palette artifacts?
• Common disadvantage: Capacity is severely limited and independent of the image size
Palette Image Steganographic Techniques• The second approach has high capacity
but is generally difficult to use without distorting the stego image.– EZ Stego method – Method proposed by Fridrich
EZ stego method
…
Sorted paletteOriginal image
),,( 000 bgr
),,( NNN bgr
),,( 111 bgr 1 2 4 …
3 8 6
Indexed image
EZ stego method
1 2 4 …
3 8 6
1 0 1 …
1 0 1Indexed image
Secret image
LSB substitution
1 2 5 …
3 8 7
Indexed image
EZ stego method
1 2 5 …
3 8 7
Indexed image
Stego-image
…
),,( 000 bgr
),,( NNN bgr
),,( 111 bgr
Color Distance
The color distance between
is represented as:
,
C1
Fridrich’s steganographic method
),,( 10
10
10
CCC bgr
Partition into two sub-codebook
C0
…),,( 0
00
00
0CCC bgr
…
),,( 1''
1''
1''
CL
CL
CL bgr
02mod)( iii bgr
…
),,( 000 bgr
),,( 111 LLL bgr
),,( 111 bgr
12mod)( iii bgr
),,( 0'
0'
0'
CL
CL
CL bgr
Fridrich’s steganographic method
Compute the parity
Find the closest color entrywith correct parity andreplace the original index
EZ v.s. Fridrich’s
(a) (b) (c)
An example to illustrate the results of palette image steganography by using different methods (a) the original image (b) the stego-image generated by EZ stego method; (c) the stego-image generated by Fridrich’s method.
The proposed method
The Proposed Method
• Definition 1: Let i, j be two color entries in palette P with Ci=( ri , gi, bi ) and Cj=( rj , gj, bj) respectively, the color distance between Ci and Cj (in Euclidean norm) is denoted as
d(i,j)= • Definition 2: Let f be a palette-based image with palette size
L (with entries 0 to L-1), the occurrence frequency for each entry in f is denoted as N(i), where i=0 to L-1
• Definition 3: In a palette P, Cx is the closest color for Cy, if entry x satisfies d(x, y)=Min{ d(n, y): n=0,1,…,L-1 and n≠y } where L is the size of P. We say that x is the first referenced entry for entry y in P and is denoted as
x= R first(y)
222 )()()( jijiji bbggrr
The Proposed Method
• Definition 4: In a palette P, Cx is the closest color for Cy with different parity (parity bit of the color Cx (rx, g
x, bx ) is rx+gx+bx mod 2), if entry x satisfies
d(x, y)=Min{ d(n, y): n=0,1,…,L-1 and (ry+rn+gy+gn+by+bn) mod 2=1} We say that x is the first referenced entry for entry y in P and is denoted as
• Definition 5: In a palette P, if Cx is the first closest color for Cy with different parity, then the second closest color Cz for color Cy is defined as follows:
d(z, y)=Min{ d(n, y): n=0,1,…,L-1. n≠x and (ry+rn+gy+gn+by+bn) mod 2=1} We say that z is the second referenced entry for entry y in P, and is denoted as
)(yRx firstDP
)(sec yRz ondDP
The proposed method (Evaluate the cost of removing an
entry)
The cost of removing an entry:
The proposed method (Evaluate the cost of removing an
entry)
N(i)
…
20
123
28
480
The cost of Fridrich’s method:
C0
…
),,( 00
00
00
CCC bgr
),,( 0'
0'
0'
CL
CL
CL bgr
C1
),,( 10
10
10
CCC bgr
…
),,( 1''
1''
1''
CL
CL
CL bgr
The proposed method (Evaluate the cost of removing an
entry)The cost of removing an entry:1. Self cost
K=
N(i)
…
20
123
28
480
C0
…
),,( 00
00
00
CCC bgr
),,( 0'
0'
0'
CL
CL
CL bgr
C1
),,( 10
10
10
CCC bgr
…
),,( 1''
1''
1''
CL
CL
CL bgr
The proposed method (Evaluate the cost of removing an
entry)The cost of removing an entry:2. Reference cost
N(k)
…
23
128
N(i)
…
20
123
28
480
C0
…
),,( 00
00
00
CCC bgr
),,( 0'
0'
0'
CL
CL
CL bgr
C1
),,( 10
10
10
CCC bgr
…
),,( 1''
1''
1''
CL
CL
CL bgr
The proposed method (Evaluate the benefit of reating a new enry)
The benefit of creating a new entry:
A new entry)1,,( 00
00
00 CCC bgr
N(i)
…
2000
123
28
480
C0
…
),,( 00
00
00
CCC bgr
),,( 0'
0'
0'
CL
CL
CL bgr
C1
),,( 10
10
10
CCC bgr…
),,( 1''
1''
1''
CL
CL
CL bgr
The benefit of creating a new entry from :
Improving Security
),,( zzzz bgrC
The benefit of creating a new entry from :
)( iRjwhere firstDP
Experimental Results
(a) (b) (c) (d) An example to illustrate the steganographic methods. (a) Original image “Fruit”
of size 256×256; (b) Stego-image obtained using EZ Stego method; (c) Stego-image obtained using Fridrich’s method; (d) Stego-image obtained using the proposed method with 88 iterations
Experimental Results
(a) (b) (c) (d) An example to illustrate the steganography methods. (a) Original image “S
wimmer” of size 256×256; (b) Stego-image obtained using EZ Stego method; (c) Stego-image obtained using Fridrich’s method; (d) Stego-image obtained using the proposed method with 116 iterations
The RMS errors with different iterations by employing the proposed method: (Left) for the “Fruit” image; (Right) for the “Swimmer” image
0
2
4
6
8
10
RM
S
0
5
10
15
20
25
0 1 2 5 10 20 30 50 80 100 116
iteration number
RM
S
0 10 20 30 40 50 60 70 80 88 iteration number
0
2000
4000
6000
800010000
12000
14000
16000
18000
1 11 21 31 41 51 61 71 81 91 101 111 121
Benefit
Cost
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1 11 21 31 41 51 61 71 81 91 101 111 121
Benefit
Cost
The graphs of the evaluated benefit and cost functions: (Left) for the “Fruit” image; (Right) for the “Swimmer” image
Experimental Results
RMSE (Root Mean Square Error)
Fruit Swimmer
EZ Stego 21.97 36.88
Fridrich’s Method 7.78 20.41
The proposed method 2.22 0.38
The modified method with α=0.1
2.33 0.94
The modified methodwith α=0.3
3.00 3.14
視覺祕密分享技術
• 秘密分享的動機是源於金鑰安全管理，所發展出來的密碼技術。
• 在加密系統中，主金鑰是系統安全的關鍵，解決主金鑰遺失或獨裁管理的問題而發展出來的秘密分享技術，其概念為：將機密分割為多等份，其中”足夠多”之部份即可以回復原始機密。
• 視覺祕密分享技術就是秘密分享所發展出來的視覺密碼技術。
視覺秘密分享 (Visual Secret Sharing)
• (k,n) VSS 定義：產生 n 個 shares ，重疊其中 k張，即可顯示藏入其中的訊息
• 一個 (k,n)VSS 的結構包含 C0和 C1兩個布林矩陣。• C0={ 所有對”白色矩陣”的”行向量”重新排列的矩陣 }
• C1={ 所有對”黑色矩陣”的”行向量” 重新排列的矩陣 }
• 分配者可以任意選 C0/C1中的任ㄧ個矩陣來分享( 對應 ) 原始秘密影像中任一點白色 /黑色的像素。
(k,n) VSS 示意圖
視覺秘密分享 -(2, 2)VSS
,
}
(1,2,n) VSS
(1,2,n) Scheme先將 n張秘密影像，顯示在n張分享子圖片中，然後再任意由 n張分享子圖片中，挑出兩張作重疊動作，就可以看到其他秘密影像。
視覺秘密分享 - (1,2,2) VSS
+ =
視覺秘密分享 - (1→4) VSS
(1→4) VSS編碼簿
Top Down VSS
• 利用 (2,2)VSS ，當我們將兩張投影片 share1與 share2 重疊時可以得到一張秘密影像 P1 ，將其中一張 share翻轉 (由正面轉至背面 ) ，再次重疊時，可以得到另一張秘密影像 P2 。
+
+
Top Down VSS
Top Down -VSS編碼簿
實例一
實例二
彩色視覺秘密分享
Properties of Digital watermarking
techniques• The performance of any watermarking
algorithm should be evaluated by the following factors: – Imperceptibility : the watermark should not be
noticeable to the viewer/listener nor should it degrade the quality of the content.
– Robustness: the watermark should still be detected after the image has undergone changes. Images should be robust to transformations that include common signal distortions as well as digital to analog and analog to digital conversion, and geometric distortions.
– Capacity: the watermarking technique must be capable of allowing multiple watermarks to be inserted in an image with each watermark still being independently verifiable.
Factors of Digital watermarking
techniques
Categories of watermarking schemes
• Those requiring both the original image and the secret keys for the watermark bit decoding are called private watermark schemes.
• Those requiring only secret keys but not the original image are called public or blind watermark schemes
• Those requiring the secret keys and the watermark bit sequence are called semi-blind watermark schemes
Categories of attacks• Many of watermarking techniques focus
on robustness against image processing attacks like : JPEG or other compression techniques, noise addition, and low-pass filtering. Schemes based on frequency domain do well on this matter.
• However many handy image processing tools make the geometric attacks and cropping attacks easy to be performed without suffering too much efforts nor causing perceptible distortion
Synchronization problem caused by geometric attacks
• To develop a robust watermarking technique which can survive from geometric attacks such as rotation and scaling becomes an important issue
Previous work on synchronization problem
• Few of exists methods considered the synchronization detection problem
• Bas et al. uses a set of feature points on images and performs a Delaunay tessellation on the set of points. The watermark is embedded using a classical additive scheme inside each triangle of the tessellation
original object
compute center and principle axes
shape self-similarity segmentation
watermark embedder
binary mask
source frames
watermarked object
test object
compute center and principle axes
shape self-similarity segmentationwatermark extracter
attacks
watermark
watermark
key
key
affine transform
affine transform
Shape normalization
• Step 1: Compute the second-ordered central moments of S, including .
• Step 2: Calculate the principle angle φ of S from the central moments with:
• Step 3: Find out the minimum bounding rectangle of the rotated object S with rotated around its mass center c at an angle of φ.
)2
(tan2
1
0220
111
112002 , and ,,
x y
qpqp yyxx )()(,
Shape normalization• Step 4: Construct an affine transformation T to map
S bounded in the rectangle into a normalized object S’ bounded in a square with edge lengthλ:
where r, s are the edge width and length of the bounding rectangle and h, k are the translations to fix the origin of the plane at the centroid of the shape
k
h
y
x
sr
sry
xwT
cossin
sincos
self-similarity segmentation
• Step 5: Divide the transformed object into n co-centered regions. We repeat dividing S into n sub-regions by resizing S with a scaling ratio sequence,
to keep the number of pixels in each sub-region nearly the same, where . These segmented sub-regions comprise a set of self-similarity shapes.
lnll /1,,/2,/1
nl
Shape self-similarity
1
2
3
4
n=41
2
3
4
5m=5
Total: 5*4= 20 blocks in the shape mask
1-n ..., 3, 2, 1, ifor
:
n
iscale
1-m ..., 3, 2, 1, ifor
2
:angle ivide
m
d
self-similarity segmentation
• Step 6: Divide each co-centered region into m small blocks by generating m rays from the object center to the boundary with an angleθincrement, where
m/2
Watermark embedding
• After the affine transform and self- similarity segmentation, The object f is transformed into object f’
• f’ is now divided into disjoint mxn image blocks ,B’1,B’2,…,B’mxn
• Map the mxn blocks B’1,B’2,…,B’mx
n of f’ to the mxn blocks B1,B2,…,Bmxn of f according to the transformation T
Watermark embedding
• If the embedded watermark is k bits data, We partition all blocks Bi into k groups,
• Each group Gi can be denoted as
kGGG ,...,, 21
},1)1(:{ Njk
nmij
k
nmiBG ji
Watermark embedding• Divid Gi into a pair of subsets Ci and Di wh
ere Ci contains all the blocks Bj in Gi which have odd indices; and Di contains blocks which have even indices
if wi =1 if wi =0 Where are the mean of gray valu
es of their member blocks • Modification of the values of by
adding or subtracting a fix value to each pixel contained in relative blocks to fit the requirement of above rules.
0 ii DC0 ii CD
ii DC and
ii DC and
Watermark extraction
• To extract watermarks from a test object, the same normalization and segmentation procedures in watermark embedding are performed with the specified used secret keys.
• Compute the corresponding blocks and extract watermarks easily by comparing , if then watermark bit “1” is extracted, otherwise bit “0” is extracted.
ii DC and ii DC
Experimental results
• An object image “Russian Nested Doll” is used , its principle angle is obtained asφ=1.29
• Corresponding normalized object by transformed
• Shape segmentation result • 12 different co-center sub-regions with
different gray levels and each sub-region is divided into 36 small blocks
• 24 bits of watermark is embedded
Experimental results on a “Russian Doll” object.
(a): The original image of test object “Russian Doll”. (b): The normalized form using the proposed method. (c): Shape self-similar segmentation using the proposed method. (d):The corresponding detector response using the proposed method. (e):The corresponding detector response using the method of Lu.
(a)
(b) (c)
(d) (e)
• The end
Data Hiding Scheme for Medical Images
• DATA SOURCE: 17th International Conference on Electronics, Communications and Computers (CONIELECOMP 2007)
• Medical image watermarking requires extreme care when embedding additional data within the medical images because the additional information must not affect the image quality
• CR, MR and CT, obtain images that can be stored in digital formats such as (DICOM files) which are related with data of patients
What
Where
How-embedding process
How-extracting process
Example
Data Hiding Scheme for Authentication of Electronic Clinical Atlas
