CN103854251A - Volume data multi-watermark method based on three-dimensional DWT-DCT (3D Wavelet Transform-Discrete Cosine Transformation) perceptual hashing - Google Patents

Abstract

The invention discloses a volume data multi-watermark method based on three-dimensional DWT-DCT (3D Wavelet Transform-Discrete Cosine Transformation) perceptual hashing, and belongs to the field of multimedia signal processing. The method comprises the following steps: firstly, performing 3DDWT-DCT on medial volume data, selecting previous 4*4*4 coefficients, further performing 3D-IDCT (Three Dimensional-Inverse Discrete Cosine Transformation), subsequently extracting a perceptual hashing value with a robust characteristic in an inverse transformation coefficient through perceptual hashing, associating a multi-watermark sequence with the perceptual hashing value to obtain a string of two-value key sequences, subsequently storing the two-value key sequences in a third party, extracting the three-dimensional DWT-DCT perceptual hashing value from volume data to be tested, and associating the extracted perceptual hashing value with the two-value key sequences stored in the third party so as to extract multiple watermarks. The method is a volume data multi-digital watermarking technique based on three-dimensional DWT-DCT perceptual hashing, and has good robust property, and the content of original volume data is not changed by embedding of multiple watermarks.

Description

The many water mark methods of volume data based on three-dimensional DWT-DCT perception Hash

Technical field

The present invention relates to a kind of volume data multiple digital digital watermark based on three-dimensional DWT-DCT perception Hash, is a kind of multi-media data protection method, belongs to field of multimedia signal processing.

Technical background

Utilize internet can realize distance medical diagnosis, when medical volume data carries out remote transmission on network, be recorded in the personal information of the patient on medical picture, be easy to be revealed.How to protect patient individual privacy, the data such as personal information, patient's electronic health record in patient's the medical imaging such as CT, MRI are not revealed, this problem is along with the universal of internet becomes day by day serious.Encryption method now and access control have been difficult to meet the requirement of medical volume data information security, if be embedded in medical picture using personal information as digital watermarking, be that medical image digital watermark technology (Medical Image Watermarking is called for short MIW) can address this problem effectively.

Digital watermark technology is the copyright protection for the Digital Media on internet at first; utilize now the feature such as invisibility, robustness of digital watermarking; patient's personal information can be hidden in its medical image, to guarantee its safe transmission on the internet.The appearance of medical image digital watermarking, when distance medical diagnosis, the required relevant patient data of remote operation are transmitted on the internet, can effectively protect patient's privacy, avoids patient's data to be tampered.

The research of the current volume data multiple digital watermarking algorithm for resist geometric attacks is less.And volume data exists in a large number in medical image, as: CT, MRI image be all by the volume data that form of section, therefore studies how in volume data, to embed multiple digital watermark significant, and for medical volume data, be generally not allow to revise its content.This has improved difficulty for embed many watermarks in volume data again.

In addition, image compression standard JPEG 2000 of future generation is based on wavelet transformation.Therefore,, to utilizing three-dimensional DWT-DCT perception Hash, in volume data, the work of embed watermark has greater significance.

Summary of the invention

The object of the invention is to propose a kind of based on three-dimensional DWT-DCT perception Hash; many watermarking inset and distills of volume data method that realization can be resisted geometric attack and can be resisted again conventional attack; it has higher robustness; and the embedding of many watermarks does not affect the voxel data value of initial body data; be a kind of zero watermarking project, thereby protected preferably three-dimensional data.

To achieve these goals, the present invention is performed such: first medical volume data is carried out to overall 3D-DWT conversion, obtain " approximation coefficient " and " detail coefficients ", the wavelet transformation of this similar two dimensional image, " approximation coefficient " represents the low frequency characteristic of volume data, reflection be the main exterior contour of volume data; " detail coefficients " represents the high frequency characteristics of volume data, reflection be the detail of the high frequency of volume data.The resist geometric attacks ability of wavelet transformation itself is poor, therefore, we first carry out 3 D wavelet transformation (DWT) to volume data, and then " approximation coefficient " of reflection low frequency characteristic carried out to overall cosine transform (DCT), choose front 4 × 4 × 4 coefficients, carry out inverse transformation, 3D-IDCT, asks for the mean value of coefficient after inverse transformation again, then the coefficient after each inverse transformation and mean value are compared, carry out two-value quantification treatment, be more than or equal to mean value, be designated as 1; Be less than mean value, be designated as 0, obtain the perception cryptographic hash of volume data.Perception Hash has robustness and sentience not, and multi-watermarking sequence is associated with the perception cryptographic hash of extraction, realizes many watermarking inset and distills;

Now be elaborated as follows to method of the present invention:

First select one group of significant binary sequence as many watermarks that will embed medical volume data, be designated as W ^g={ w ^g(j) | w ^g(j)=0,1; 1≤j≤L}; Meanwhile, choose the MRI volume data carrying in Matlab as primitive medicine volume data, be expressed as: F={f (i, j, k) | f (i, j, k) ∈ R; 1≤i≤M, 1≤j≤N, 1≤k≤P}.Wherein, f (i, j, k) represents voxel (Voxel) data value of primitive medicine volume data, and the grey scale pixel value in this similar two dimensional image, for the purpose of facilitating, is established M=N.

Part I: the embedding of many watermarks

1), by three-dimensional DWT-DCT perception hash algorithm, obtain the perception cryptographic hash H (j) of a resist geometric attacks of initial body data;

First initial body data F (i, j, k) is carried out to 3 D wavelet transformation, obtain ll channel coefficient FA _l, then to ll channel FA _lcarry out overall three-dimensional dct transform, obtain DWT-DCT matrix of coefficients FD (i, j, k), in matrix of coefficients FD (i, j, k), choose front 4 × 4 × 4 coefficient FD ₄(i, j, k), then to the matrix of coefficients FD selecting ₄(i, j, k) carries out three-dimensional anti-dct transform, obtains the coefficient FID (i after inverse transformation, j, k), ask for the mean value of coefficient after inverse transformation, then the coefficient after each inverse transformation and mean value are compared, carry out two-value quantification treatment, be more than or equal to mean value, be designated as 1; Be less than mean value, be designated as 0, obtain the perception cryptographic hash H (j) of volume data.Main process is described below:

FA _L(i,j,k)=DWT3(F(i,j,k))

FD ₄(i,j,k)=DCT3(FA _L(i,j,k))

FID(i,j,k)=IDCT3(FD ₄(i,j,k))

H(j)=BINARY(FID(i,j,k))

2) utilize HASH function, embed multi-watermarking, generate the two-value key sequence Key containing many watermark informations ^g(j);

Key ^g(j)=H(j)⊕W ^g(j)

Key ^g(j) be perception cryptographic hash H (j) and the many watermark sequences W by volume data ^g(j), generate by the conventional Hash function of cryptography.Preserve Key ^g(j) while, extracting many watermarks below, to use.By by Key ^g(j) apply for to third party as key, to obtain entitlement and the right to use of medical volume data, reach the object of copyright protection.And the embedding of many watermarks does not affect the quality of primitive medicine volume data, it is a kind of zero watermarking project.

Part II: the extraction of many watermarks

3) the perception cryptographic hash H ' that obtains volume data to be measured (j);

If volume data to be measured is F ' (i, j, k), obtain ll channel coefficient FA ' through 3 D wavelet transformation _l, then to ll channel FA ' _lcarry out overall three-dimensional dct transform, obtain matrix of coefficients FD ' (i, j, k), choose the front 4x4x4 matrix of coefficients of low frequency part, then carry out anti-dct transform, again by above-mentioned steps 1) similar method, try to achieve the perception cryptographic hash H ' of volume data to be measured (j);

FA’ _L(i,j,k)=DWT3(F’(i,j,k))

FD’ ₄(i,j,k)=DCT3(FA’ _L(i,j,k))

FID’(i,j,k)=IDCT3(FD’ ₄(i,j,k))

H’(j)=BINARY(FID’(i,j,k))

4) utilize be present in third-party two-valued function key sequence Keyg (j) and volume data to be measured perception cryptographic hash H ' (j), extract many watermarks Wg ' (j);

W ^g’(j)=Key ^g(j)?H’(j)

According to the logic key sequence Key generating in the time embedding many watermarks ^g(j) and the perception cryptographic hash H ' of volume data to be measured (j), the many watermarks W that utilizes Hash Functional Quality to extract to contain in volume data to be measured ^g' (j).Again according to W ^gand W ^g' degree of correlation differentiate whether have watermark embed, thereby confirm the entitlement of volume data to be measured and the safety issue of sufferer information.

The present invention and existing medical science multi-watermark technology relatively have following advantage:

First, because the present invention is the multiple digital digital watermark based on three-dimensional DWT-DCT perception hash algorithm, confirm by experimental data below, these many watermarks not only have stronger anti-conventional attack ability, and have stronger resist geometric attacks ability; And the embedding of many watermarks does not affect the voxel data value of initial body data, be a kind of zero digital watermark, better must protect medical volume data.This characteristic, especially has very high practical value at aspects such as medical image processing, and usable range is wide, and can realize embedding and the extraction of large watermark.

We illustrate from theoretical foundation and experimental data below:

1) perception Hash

Perception Hash is the short summary of media-aware content, can be defined as following one-way function:

h=PH(I)

Wherein, PH is perception hash function, and h represents to extract by media object I the perception cryptographic hash obtaining.Generally, perception cryptographic hash h is a binary vector H (j); The fundamental characteristics that perception hash function possesses is:

(a) summary: the shared storage space of perception cryptographic hash should be as much as possible little;

(b) perception robustness: the media object that perceived content is identical or close should be shone upon and be obtained identical or close perception cryptographic hash;

(c) anti-collision: the media object that perceived content is different can not be shone upon and be obtained identical or close perception cryptographic hash.

(d) one-way: should be not attainable on calculating by the anti-perceived content that pushes away media object of perception cryptographic hash;

The application such as mark that perception Hash is multimedia digital content, retrieval, authentication provide safe and reliable technical support to become gradually the study hotspot of multimedia signal dispose and multi-media safety and association area.

2) 3 d-dem wavelet transformation (DWT)

One deck decomposable process of 3 D wavelet transformation as shown in Figure 1, L, H in Fig. 1 represents respectively low-frequency component and the radio-frequency component of medical volume data through obtaining after low frequency and High frequency filter, similar with the wavelet transformation of two dimensional image, medical volume data, after 3 D wavelet transformation, is broken down into " approximation coefficient " LLL who represents volume data low frequency characteristic ₁(low frequency 3-d subband), and represent that " detail coefficients " (high frequency 3-d subband) of the high-frequency information of this volume data, subscript " 1 " represent that the ground floor of three-dimensional DWT decomposes; The schematic three dimensional views of the 3 D wavelet transformation (two-layer) of one individual data items is shown in Fig. 4, the section that Fig. 2 is volume data, the three-dimensional imaging that Fig. 3 is volume data, the 3 D wavelet transformation (two-layer) that Fig. 4 is volume data.

3) Three-dimensional DCT (3D-DCT)

Three-dimensional dct transform formula is as follows:

Corresponding size is M × N × P volume data, and 3 d-dem cosine direct transform (DCT) formula is as follows:

$F(u,v,w)=c\left(u\right)c\left(v\right)c\left(w\right)[\underset{x=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{y=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{p=0}{\overset{P-1}{\mathrm{\Σ}}}f(x,y,z)\·\cos \frac{(2x+1)\mathrm{u\π}}{2M}\cos \frac{(2y+1)\mathrm{v\π}}{2N}\cos \frac{(2z+1)\mathrm{w\π}}{2P}]$

u＝0,1,...,M-1;v＝0,1,...,N-1;w＝0,1,...,P-1;

In formula

$c\left(u\right)=\left\{\begin{array}{cc}\sqrt{1/M}& u=0\\ \sqrt{2/M}& u=\mathrm{1,2},...,M-1\end{array}\right.$

$c\left(v\right)=\left\{\begin{array}{cc}\sqrt{1/N}& v=0\\ \sqrt{2/N}& v=\mathrm{1,2},...,N-1\end{array}\right.$

$c\left(w\right)=\left\{\begin{array}{cc}\sqrt{1/P}& w=0\\ \sqrt{2/P}& w=\mathrm{1,2},...,P-1\end{array}\right.$

Here, f (x, y, z) is voxel (voxel) data value that volume data V locates at (x, y, z), and F (u, v, w) is the 3D-DCT conversion coefficient that this voxel data is corresponding.

3 d-dem cosine inverse transformation (IDCT) formula is as follows:

$f(x,y,z)=\underset{u=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{v=0}{\overset{N-1}{\mathrm{\Σ}}}\underset{w=0}{\overset{P-1}{\mathrm{\Σ}}}[c\left(u\right)c\left(v\right)c\left(w\right)F(u,v,w)\cos \frac{(2x+1)\mathrm{u\π}}{2M}\cos \frac{(2y+1)\mathrm{v\π}}{2N}\cos \frac{(2z+1)\mathrm{w\π}}{2P}]$

x＝0,1,...,M-1;y＝0,1,...,N-1;z＝0,1,...,P-1

Wherein, (x, y, z) is spatial domain sampled value; (u, v, w) is frequency field sampled value.The acquisition of medical volume data can be by CT and MRI(Magnetic Resnane Iamge, magnetic resonance imaging), volume data (Volume data) is made up of the section (slice) of many layers, and each section is a two dimensional image, size is M × N, and the number of plies of section is P.

4) choosing method of volume data perception hash function

The main cause of most of many watermarking algorithms resist geometric attacks ability is at present: people are embedded in multiple digital watermark in voxel or conversion coefficient, and the slight geometric transformation of volume data usually can cause the larger variation suddenly of voxel data value or transform coefficient values.The many watermarks that are embedded in like this in volume data are just attacked easily.If can find the perception cryptographic hash of an antimer data geometrical feature, in the time there is little geometric transformation in volume data, can there is not obvious sudden change in this perception cryptographic hash, then we are associated the perception cryptographic hash of the multiple digital watermark that will embed and this volume data, just can solve preferably the robustness problem of watermark.The experimental data that we choose some conventional attacks and geometric attack is shown in Table 1.The former figure that is used as test in table 1 is Fig. 5, it is a section (getting the tenth) of a MRI volume data carrying in matlab, in table 1, " the 1st row " demonstration is volume data type under attack, this sectioning image being subject to after conventional attack is shown in Fig. 6 to Fig. 8, and Fig. 9 to Figure 12 is shown in the three-dimensional imaging that conventional attack is corresponding; The sectioning image being subject to after geometric attack is shown in Figure 13 to Figure 16, and Figure 17 to Figure 20 is shown in its corresponding three-dimensional imaging.Y-PSNR (PSNR) after volume data that what " the 2nd row " of table 1 represented is is under attack; " the 3rd row " of table 1 is to choose arbitrarily eight coefficient values such as " F (1, Isosorbide-5-Nitrae), F (1,2,1) " in the real part of conversion coefficient from anti-dct transform to " the 10th row " expression." the 11st row " of table 1 are after DWT-DCT perception hash algorithm two-value quantification treatment, to obtain the mean value coming.For conventional attack or geometric attack, may there are some conversion in these coefficient values F (1, Isosorbide-5-Nitrae), F (1,2,1) etc., but the magnitude relationship of it and mean value is still constant, and we will be more than or equal to mean value, be designated as 1; Be less than mean value, be designated as 0, so for initial body data, coefficient value F (1,1,4), F (1,2,1) etc. the cryptographic hash sequence of correspondence is: " 00010101 ", specifically, in the 12nd row of table 1, observe these row can find, no matter conventional attack or this symbol sebolic addressing of geometric attack are similar with the maintenance of initial body data, all larger with initial body data normalization related coefficient, be 1.0, in table 1 " the 13rd row " (having got 8 anti-dct transform coefficient symbols of three-dimensional here for the purpose of convenient).

For the perception cryptographic hash that further proves to extract as stated above, having can not collision property, for different volume datas, and perception cryptographic hash difference; We process them by three-dimensional DWT-DCT perception hash algorithm again different tested objects (seeing Figure 21 to Figure 27).From angle of statistics, front 8 × 8 × 4 DWT-DCT coefficients are got here.And the cryptographic hash sequence of obtaining every individual data items related coefficient each other, result of calculation is as shown in table 2.

Table 1 is subject to the changing value after different attack based on DWT-DCT volume data perception cryptographic hash

* DWT-DCT and perception Hash are processed rear coefficient unit 1.0e+002

Related coefficient (vector length 256bit) between the different volume data perception of table 2 cryptographic hash

?	Ha	Hb	Hc	Hd	He	Hf	Hg
								Ha	1.00	0.62	0.46	-0.10	-0.51	0.11	0.14
Hb	0.62	1.00	0.26	0.08	-0.51	0.18	0.18
								Hc	0.46	0.26	1.00	-0.05	-0.37	-0.12	-0.14
Hd	-0.10	0.08	-0.05	1.00	-0.10	0.09	0.12
								He	-0.51	-0.51	-0.37	-0.10	1.00	-0.24	-0.21
Hf	0.11	0.18	-0.12	0.09	-0.24	1.00	0.69
								Hg	0.14	0.18	-0.14	0.12	-0.21	0.69	1.00

As can be seen from Table 2, the related coefficient maximum between first perception cryptographic hash self, is 1.00; Secondly, the related coefficient between Figure 26 and Figure 27 also more greatly 0.69, and the volume data that these two figure are two similar livers of shape; Figure 21 and Figure 22, related coefficient is 0.62, also larger, be the third-largest related coefficient, and these two heads that figure is human body is also more similar in table.Facies relationship numerical value between other perception cryptographic hash is less, this with our eye-observation to be consistent, the perception cryptographic hash that this explanation is extracted by the method for this invention, has reflected the main resemblance of volume data, has good robustness and sentience not.

In sum, we utilize three-dimensional DWT-DCT perception cryptographic hash, obtain embedding and the extracting method of the many watermarks of volume data.

Accompanying drawing explanation

Fig. 1 is 3 D wavelet transformation schematic diagram (one deck).

Fig. 2 is a section of initial body data.

Fig. 3 is three-dimensional imagings corresponding to initial body data.

Fig. 4 is the result demonstration of initial body data being carried out to 3 D wavelet transformation (two-layer).

Fig. 5 is a section (acquiescence is the 10th section of volume data) of initial body data.

Fig. 6 is the sectioning image after 10% Gauusian noise jammer.

Fig. 7 is the sectioning image after JPEG compression (compression quality is 2%).

Fig. 8 is the sectioning image (filtering parameter is [5x5]) after medium filtering.

Fig. 9 is three-dimensional imagings corresponding to initial body data.

Figure 10 is that to be subject to intensity be the three-dimensional imaging that 10% Gauss disturbs rear correspondence to volume data.

Figure 11 is corresponding three-dimensional imaging after JPEG compression (compression quality is 2%).

Figure 12 is three-dimensional imaging (filtering parameter is [5x5]) corresponding after medium filtering.

Figure 13 is the sectioning image through up time rotation 20 degree.

Figure 14 is the sectioning image through 0.5 times of convergent-divergent.

Figure 15 is that vertical direction moves down 10% sectioning image.

Figure 16 is that Z-direction is sheared first sectioning image after 10%.

Figure 17 is the three-dimensional imaging of up time rotation 20 degree.

Figure 18 is that zoom factor is 0.5 three-dimensional imaging.

Figure 19 is that vertical direction moves down 10% three-dimensional imaging.

Figure 20 is that Z-direction is sheared 10% three-dimensional imaging.

Figure 21 is the three-dimensional imaging of volume data MRI_1.

Figure 22 is the three-dimensional imaging of volume data MRI_2.

Figure 23 is the three-dimensional imaging of volume data MRI_3.

Figure 24 is the three-dimensional imaging of volume data Teddy bear.

Figure 25 is the three-dimensional imaging of volume data Tooth.

Figure 26 is the three-dimensional imaging of volume data Liver_1.

Figure 27 is the three-dimensional imaging of volume data Liver_2.

Figure 28 is the many watermark sections that do not add while interference.

Figure 29 is the three-dimensional reconstruction figure not adding while interference.

Figure 30 does not add many watermarks of extracting while interference.

Figure 31 is the sectioning image (Gaussian noise intensity 10%) after Gauusian noise jammer.

Figure 32 is the three-dimensional reconstruction figure (Gaussian noise intensity 10%) after Gauusian noise jammer.

Figure 33 is many watermarks (Gaussian noise intensity 10%) of extracting after Gauusian noise jammer.

Figure 34 is the sectioning image (compression quality parameter is 5%) after JPEG compression.

Figure 35 is the volume data three-dimensional imaging (compression quality parameter is 5%) after JPEG compression.

Figure 36 is many watermarks (compression quality parameter is 5%) of extracting after JPEG compression.

Figure 37 is the sectioning image (filtering parameter is [5x5], and filter times is 1 time) after medium filtering.

Figure 38 is the three-dimensional imaging (filtering parameter is [5x5], and filter times is 1 time) of the volume data after medium filtering.

Figure 39 is many watermarks (filtering parameter is [5x5], and filter times is 1 time) of extracting after medium filtering.

Figure 40 is the sectioning image after up time rotation 20 degree.

Figure 41 is the three-dimensional imaging of volume data after up time rotation 20 degree.

Figure 42 is many watermarks of extracting after up time rotation 20 degree.

Figure 43 is that zoom factor is 0.5 sectioning image.

Figure 44 is that zoom factor is 0.5 three-dimensional imaging.

Figure 45 is that zoom factor is many watermarks of extracting for 0.5 o'clock.

Figure 46 vertically moves down 5% sectioning image.

Figure 47 vertically moves down 5% three-dimensional imaging corresponding to volume data.

Figure 48 is the many watermarks that vertically move down 5% rear extraction.

Figure 49 is after Z-direction shears 5%, first sectioning image of volume data.

Figure 50 is after Z-direction shears 5%, the three-dimensional imaging of volume data.

Figure 51 is after Z-direction shears 5%, many watermarks of extraction.

Figure 52 is that distortion frequency factor is the sectioning image of 13 o'clock.

Figure 53 is that distortion frequency factor is the three-dimensional imaging of the volume data of 13 o'clock.

Figure 54 is that distortion frequency factor is many watermarks of extracting for 13 o'clock.

Embodiment

Emulation platform is Matlab2010a, use 1000 groups of independently binary pseudo-random (value+1 or-1), every group of sequence length is 64bit, and in these 1000 groups of data, we appoint three watermark sequences that extract three groups (we choose the 300th, 500 and 700 groups) conduct embedding here.Below in conjunction with accompanying drawing, the invention will be further described, and Fig. 5 is shown in a section of primitive medicine volume data, is to take from the nuclear magnetic resonance 3-D view volume data (MRI.mat) carrying in matlab, and the size of volume data is 128x128x27, sees Fig. 9.Initial body data are expressed as F (i, j, k), wherein 1≤i, j≤128; 1≤k≤27, corresponding three-dimensional DWT-DCT matrix of coefficients is FD (i, j, k), wherein 1≤i, j≤128; 1≤k≤27.Consider robustness and the many watermarks of disposable embedding capacity we get front 4 × 4 × 4 coefficients.Carry out 3D-IDCT conversion again, and ask for the mean value of coefficient after inverse transformation, then by mean value, the coefficient after to inverse transformation carries out two-value quantification treatment, obtains the cryptographic hash H (j) of volume data.Detect W by many watermarking algorithms ^g' (j) after, we by calculate normalized correlation coefficient NC (Normalized Cross Correlation) judged whether watermark embed.NC, as the output of watermark detector, reflects according to the large I of this value whether watermark exists.

Do not add while interference, Figure 28 is the sectioning image (acquiescence is selected the tenth section here, and test is made up of 27 sections altogether by volume data) not adding while interference;

Figure 29 is the volume data three-dimensional imaging not adding while interference;

Figure 30 does not add many watermarks of extracting while interference, can see NC1=1.00, NC2=1.00, NC3=1.00, can accurately extract many watermarks.

We judge anti-conventional attack ability and the resist geometric attacks ability of this multiple digital water mark method by specific experiment below.

First test the ability of the anti-conventional attack of these many watermarking algorithms.

(1) add Gaussian noise

Use imnoise () function to add Gaussian noise in many watermarking images.

Table 3 is experimental datas of the anti-Gauusian noise jammer of many watermarks.Therefrom can see, when Gaussian noise intensity is up to 25% time, the PSNR of many watermarks volume data is down to 0.07dB, many watermarks of at this moment extracting, and related coefficient NC1=0.97, NC2=0.97, NC3=0.97, still can accurately must extract many watermarks.The anti-Gaussian noise ability that this explanation adopts this invention to have.

Sectioning image when Figure 31 is Gaussian noise intensity 10% is visually very fuzzy;

Figure 32 is corresponding volume data three-dimensional imaging, visually very fuzzy;

Figure 33 is many watermarks of extracting, and can accurately must extract many watermarks, NC1=1.00, NC2=1.00, NC3=1.00.

The anti-Gauusian noise jammer data of watermark more than table 3

Noise intensity (%)	1	3	5	10	15	20	25
								PSNR(dB)	12.52	8.02	6.03	3.32	1.80	0.81	0.07
NC1	1.00	1.00	1.00	1.00	0.97	0.97	0.97
								NC2	1.00	1.00	1.00	1.00	0.97	0.97	0.97
NC3	1.00	1.00	1.00	1.00	0.97	0.97	0.97

(2) JPEG compression is processed

Adopt image compression quality percentage, as parameter, many watermarks volume data is carried out to JPEG compression; Table 4 is the anti-JPEG compression experiment of many watermarks volume data data.When compression quality is only 2%, at this moment compression quality is lower, still can extract many watermarks, NC1=0.97, NC2=0.96, NC3=0.97.

Figure 34 is that compression quality is 5% sectioning image, and blocking artifact has appearred in this figure;

Figure 35 is corresponding volume data three-dimensional imaging, and three-dimensional blocking artifact has appearred in this figure;

Figure 36 is many watermarks of extracting, and NC1=1.00, NC2=1.00, NC3=1.00, can

Accurately extract many watermarks.

The anti-JPEG compression experiment of watermark more than table 4 data

Compression quality (%)	2	4	8	10	20	40	60	80
									PSNR(dB)	16.57	17.82	20.21	21.20	23.10	25.06	26.61	29.31
NC1	0.97	1.00	1.00	1.00	1.00	1.00	1.00	1.00
									NC2	0.96	1.00	1.00	1.00	1.00	1.00	1.00	1.00
NC3	0.97	1.00	1.00	1.00	1.00	1.00	1.00	1.00

(3) medium filtering processing

Table 5 is the anti-medium filtering ability of many watermarks volume data, and it can be seen from the table, when medium filtering parameter is [7x7], filtering multiplicity is 20 o'clock, still can record the existence of many watermarks, NC1=0.97, NC2=0.96, NC3=0.97.

Figure 37 is that medium filtering parameter is [5x5], the sectioning image that filtering multiplicity is 1, and image has occurred fuzzy;

Figure 38 is corresponding volume data three-dimensional imaging, and at this moment the profile such as ear is not too clearly demarcated;

Figure 39 is many watermarks of extracting, and NC1=0.97, NC2=0.96, NC3=0.97, can accurately extract many watermarks.

The anti-medium filtering experimental data of watermark more than table 5

Many watermarks resist geometric attacks ability

(1) rotational transform

Table 6 is the anti-rotation attack experimental datas of many watermarks.From table, can see that NC1=0.82, NC2=0.80, NC3=0.82, still can extract many watermarks in the time that many watermarks volume data up time is rotated 35 °.

Figure 40 is many watermarks sectioning image of up time rotation 20 degree;

Figure 41 is corresponding volume data three-dimensional imaging, and at this moment, the signal to noise ratio (S/N ratio) of many watermarks volume data is lower, PSNR=12.44dB;

Figure 42 is many watermarks of extracting, and NC1=0.91, NC2=0.90, NC3=0.90, can extract many watermarks exactly.

The anti-rotation attack experimental data of watermark more than table 6

Up time rotation	5 degree	10 degree	15 degree	20 degree	25 degree	30 degree	35 degree
								PSNR(dB)	16.54	13.97	12.98	12.44	12.04	11.68	11.33
NC1	0.97	0.97	0.97	0.91	0.91	0.82	0.82
								NC2	0.96	0.96	0.96	0.90	0.90	0.80	0.80
NC3	0.97	0.97	0.97	0.90	0.90	0.82	0.82

(2) scale transformation

Table 7 is the nonshrink attack experimental data of putting of many watermarks volume data, and when many watermarks volume data zoom factor is little to 0.2 time, related coefficient NC1=0.84, NC2=0.83, NC3=0.85, can accurately extract many watermarks as can be seen from Table 7.

Figure 43 is the many watermarks sectioning image (zoom factor is 0.5) after convergent-divergent;

Figure 44 is after convergent-divergent is attacked, the three-dimensional imaging (zoom factor is 0.5) that volume data is corresponding;

Figure 45 is after convergent-divergent is attacked, many watermarks of extraction, and NC1=0.97, NC2=0.96, NC3=0.97, can accurately must extract many watermarks.

The nonshrink attack experimental data of putting of watermark more than table 7

Zoom factor	0.2	0.5	0.8	1.2	2.0	4.0
							NC1	0.84	0.97	1.00	1.00	1.00	1.00
NC2	0.83	0.96	1.00	1.00	1.00	1.00
							NC3	0.85	0.97	1.00	1.00	1.00	1.00

(3) translation transformation

Table 8 is the anti-translation transformation experimental datas of many watermarks.When learning when level from table or vertically moving 10%, the value of NC1, NC2, NC3, all higher than 0.5, can accurately be extracted many watermarks, therefore these many water mark methods have stronger anti-translation transformation ability.

Figure 46 cuts into slices vertically to move down 10% image;

Figure 47 is after each section of volume data vertically moves down 10%, corresponding three-dimensional imaging, and at this moment PSNR=10.85dB, signal to noise ratio (S/N ratio) is lower;

Figure 48 is many watermarks of extracting, and can accurately extract many watermarks, NC1=0.89, NC2=0.88, NC3=0.87.

The anti-translation transformation experimental data of watermark more than table 8

(4) shearing attack

Table 9 is the anti-shearing attack experimental datas of many watermarks, can see, when shearing from Z-direction from table, when shearing displacement is 40%, still can extract many watermarks, NC1=0.85, NC2=0.94, NC3=0.94, illustrate that these many watermarking algorithms have stronger anti-shearing attacking ability.

Figure 49 is after shearing 5% by Z-direction, first sectioning image;

Figure 50 is the three-dimensional imaging of shearing 5% rear correspondence by Z-direction, can find the successful of shearing attack; The three-dimensional imaging of the relatively former figure in top, has cut one.

Figure 51 is many watermarks of extracting, and can accurately must extract many watermarks, NC1=1.00, NC2=1.00, NC3=1.00.

The anti-shearing attack experimental data of watermark more than table 9

Z axis is sheared (%)	2	4	6	8	10	20	40
								NC1	1.00	1.00	0.97	0.97	0.94	0.97	0.94
NC2	1.00	1.00	0.96	0.96	0.94	0.96	0.94
								NC3	1.00	1.00	0.97	0.97	0.94	0.97	0.94

(5) distortion is attacked

Table 10 is the anti-twist attack experimental datas of many watermarks, distortion parameter is the distortion factor, the distortion factor is larger, the frequency that represents distortion is higher, in the time that the distortion factor is 24, at this moment the lower PSNR=9.68dB of the signal to noise ratio (S/N ratio) of volume data, but at this moment NC1=0.94, NC2=0.94, NC3=0.94, still can extract many watermarks.And from table 10, find, in the time that the distortion factor is lower, larger on the low frequency characteristic impact of volume data, so NC value is less; And in the time that the distortion factor is larger, larger on the high frequency characteristics impact of volume data, less on the exterior contour impact of volume data, so NC value is larger; Data in table are consistent in the analysis of the medium and low frequency coefficient to volume data above with us.

Figure 52 is the sectioning image (the distortion factor is 13) after distortion is attacked;

Figure 53 is corresponding volume data three-dimensional imaging after distortion is attacked, PSNR=9.83dB, and signal to noise ratio (S/N ratio) is lower;

Figure 54 is many watermarks of extracting, and NC1=0.94, NC2=0.94, NC3=0.94, can extract many watermarks comparatively exactly.

The anti-twist attack experimental data of watermark more than table 10

Distortion frequency factor	2	3	5	7	9	13	20	24
									PSNR(dB)	10.12	10.13	10.16	9.89	9.58	9.83	9.68	9.68
NC1	0.84	0.81	0.97	0.94	0.94	0.94	0.94	0.94
									NC2	0.83	0.82	0.96	0.94	0.94	0.94	0.94	0.94
NC3	0.85	0.82	0.97	0.94	0.94	0.94	0.94	0.94

Claims (1)

1. the many water mark methods of volume data based on based on three-dimensional DWT-DCT perception Hash, it is characterized in that: first volume data is carried out to three-dimensional DWT-DCT conversion, choose front 4 × 4 × 4 coefficients, carry out again 3D-IDFT conversion, then in inverse transformation coefficient, extract the perception cryptographic hash of a robust, and many watermark sequences are associated with perception cryptographic hash, resist geometric attacks and the conventional attack of the watermark of medical volume data multiple digital are realized, these many watermark implementing method comprise embedding and extract two large divisions, amount to four steps:

Part I is the embedding of many watermarks: by the embedding operation to many watermarks, obtain corresponding two-valued function sequence Key ^g(j);

1), by three-dimensional DWT-DCT, try to achieve a perception cryptographic hash H (j) of initial body data; That is: initial body data are carried out to 3 D wavelet transformation, then pairing approximation coefficient carries out three-dimensional dct transform, the front 4x4x4 conversion coefficient of choosing low frequency carries out three-dimensional anti-dct transform, ask for the mean value of coefficient after inverse transformation, then the coefficient after each inverse transformation and mean value are compared, carry out two-value quantification treatment, be more than or equal to mean value, be designated as 1; Be less than mean value, be designated as 0, obtain the perception cryptographic hash H (j) of volume data;

2) utilize cryptography Hash Functional Quality and many watermark sequences W ^g(j), generate the two-value key sequence Key containing many watermark informations ^g(j), Key ^g(j)=H (j) ⊕ W ^g(j);

Preserve Key ^g(j), while extracting many watermarks below, to use, by Key ^g(j) apply for to third party as key, to obtain the entitlement to primitive medicine volume data;

Part II is the extraction of many watermarks: by two-valued function sequence Key ^g(j) and the perception cryptographic hash H ' of volume data to be measured (j), extract many watermarks W ^g' (j);

3), according to the method for step 1), obtain the perception cryptographic hash H ' of volume data to be measured (j);

4) utilize Hash Functional Quality and be present in third-party Key ^g(j), extract many watermarks W ^g' (j)=Key ^g(j) ⊕ H ' (j);

By W ^gand W (j) ^g' (j) be normalized Calculation of correlation factor, determine the entitlement of medical volume data.

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