CN102360487A - Geometric-attack-resistible medical-image multiple-watermark method based on DFT (Discrete Fourier Transform) - Google Patents

Abstract

The invention relates to a geometric-attack-resistible medical-image multiple-watermark method based on DFT (Discrete Fourier Transform). The method comprises the following steps of: firstly carrying out embedding of multiple watermarks, wherein the step comprises (1) carrying out DFT on the original medical image, and extracting a vector of an important visual feature of the image in conversion coefficients; (2) utilizing the feature vector and the multiple watermarks to be embedded to obtain a corresponding two-valued logic sequence by virtue of a Hash function, storing the two-valued sequence in a third party, and then carrying out extraction of multiple watermarks; (3) carrying out whole-image DFT on the medical image to be detected, and finding a visual feature vector of the image to be detected; and (4) utilizing the property of the Hash function and the two-valued logic sequence stored in the third party to extract the watermarks. In the geometric-attack-resistible medical-image robust multiple-watermark method, the visual feature vector of the medical image, an encryption technology and the third-party conception are organically combined, the embedding problem of the multiple watermarks is solved, and stronger geometric attack resistance and conventional attack resistance can be achieved so as to protect the copyright of the medical image and the secrecy of information of patients.

Description

But a kind of medical image multi-watermarking method based on the DFT resist geometric attacks

Technical field

The invention belongs to field of multimedia signal processing, relate to the multiple digital watermark technology of a kind of medical image, but specifically be a kind of medical image multi-watermarking method based on the DFT resist geometric attacks based on DFT conversion and Image Visual Feature.

Background technology

In recent years; Along with developing rapidly of computer science and technology and multimedia communication technology; Tele-medicine is universal day by day, but carries out medical image when transmission on the internet, and patient's personal information is leaked easily; Utilize the invisibility and the robustness of digital watermarking to address this problem preferably, promptly be embedded in patient's personal information in the medical image as digital watermarking.

The research to medical image digital watermarking field at present mainly concentrates on spatial domain and two aspects of transform domain (DFT, DFT and DWT), they respectively the value of some coefficients of gray scale or the transform domain of some pixel through the change spatial domain come embed watermark.

In view of singularity requirement to medical image focal zone protection, general medical digital water mark method is normal select with watermark information be embedded into image non-area-of-interest (Region of Non-interest, RONI).Region of interest ROI in the medical image (Region of Interest) refers to the focal zone that those comprise important pathological characters or diagnosis and treatment information, if in this zone embed watermark, the diagnosis that then might make the mistake.But often people will spend long time and energy when seeking ROI, and in case select wrongly, then might disturb doctor's diagnosis.

In medical digital watermark research field; The embedding of resisting geometric attack and multi-watermarking up to now is still a more insoluble problem; Multi-watermarking embedding grammar research as for being highly resistant to conventional attack and geometric attack does not simultaneously appear in the newspapers at present as yet, still belongs to blank.And in the practical application, the medical image digital watermarking usually receives this two kinds of attacks simultaneously.

Summary of the invention

But the purpose of this invention is to provide a kind of medical image multi-watermarking method based on the DFT resist geometric attacks; Visual feature vector, encryption technology and the third-party notion of medical image are combined; Need not carry out choosing of area-of-interest; Have very desirable robustness and invisibility; Solved the problem that multi-watermarking embeds effectively, solved the resistance geometric attack and resistance conventional attack problem that occur in the medical image applications simultaneously, with the copyright of protection medical image and the crypticity of sufferer information.

To achieve these goals; The present invention is performed such: based on full figure DFT conversion; In the DFT conversion coefficient; Extract the medical image visual feature vector of a resist geometric attacks, and digital watermark and cryptography are combined, realized the anti-geometry and the conventional attack of multiple digital watermarking.The method that the present invention adopted comprises watermark embedding and watermark extracting two large divisions, and first is the multi-watermarking embedding grammar, comprising: (1) obtains a visual feature vector V (j) of image through carrying out full figure DFT conversion; (2) according to the multi-watermarking W that will embed ^k(j), k=1,2 ..., n; The proper vector V (j) that n representes the watermark number that embeds and in medical image, extracts through the Hash functional operation, generates two-valued function sequence Key ^k(j), then with two-valued function sequence Key ^k(j) there is the third party.Second portion is the multi-watermarking method for distilling, comprising: the visual feature vector V ' that testing image is obtained in (3) (j); (4) there has been third-party two-valued function sequence Key in utilization ^{K (}J) and the proper vector V ' of medical image to be measured (j), extract multi-watermarking W ^k' (j).

Method of the present invention is elaborated as follows at present:

At first use W ^k(j) the indicate multi-watermarking that embeds, W ^k(j)={ w ^k(j) | w (j)=0,1; 1≤j≤L, 1≤k≤n}, the watermark length that the L representative will embed, n is the number of embed watermark.Original image be designated as F={f (i, j) | f (i, j) ∈ R; 1≤i≤N1,1≤j≤N2) }, wherein, (i, j) grey scale pixel value of expression primitive medicine image is established N1=N2=N to f.

First: watermark embedding method

1), obtains visual feature of image vector V (j) through carrying out full figure DFT conversion.

(i j) carries out full figure DFT conversion, obtains DFT matrix of coefficients FF (i to former figure F earlier; J), again to DFT matrix of coefficients FF (i, j); In the Low Medium Frequency coefficient, get preceding L value, and obtain this visual feature of image vector V (j) through the computing of DFT coefficient symbols; For the purpose of convenient, a plural number is regarded real part, two coefficients of imaginary part (imaginary part is only seen coefficient) as here, and we are with " 1 " expression (containing the situation of coefficient value for " 0 ") when coefficient value is " just "; With " 0 " expression, main process prescription was following when coefficient was negative:

FF(i，j)＝DFT2(F(i，j))

V(j)＝-Sign(FF(i，j))

2) according to watermark W ^k(j) and visual feature of image vector V (j) generate a two-valued function sequence Key ^k(j).

${\mathrm{Key}}^k\left(j\right)=V\left(j\right)\⊕W^k\left(j\right);k=\mathrm{1,2},...,n$

Key ^k(j) be by visual feature of image vector V (j) and watermark W ^k(j), generate through cryptography Hash function commonly used.Preserve Key ^kNeed use when (j), extracting watermark afterwards.Through with Key ^k(j) apply for to the third party as key,, thereby reach the purpose of protecting medical image with checking and entitlement of acquisition original image.

Second portion: watermark extracting method

3) the visual feature vector V ' that obtains testing image (j).

If testing image be F ' (i, j), through obtain after the full figure DFT conversion DFT matrix of coefficients be FF ' (i, j), by above-mentioned Step1 method, the visual feature vector V ' that tries to achieve testing image (j);

FF’(i，j)＝DFT2(F’(i，j))

V’(j)＝-Sign(FF’(i，j))

4) in testing image, extract watermark W ^k' (j).

According to the Key that generates when the embed watermark ^k(j) and the visual feature vector V ' of testing image (j), utilize Hash character can extract the watermark W of testing image ^k' (j).

$W^{k,}\left(j\right)={\mathrm{Key}}^k\left(j\right)\⊕V^{,}\left(j\right)$

Again according to W ^k(j) and W ^k' (j) degree of correlation differentiates the entitlement of testing image and the safety issue of hidden danger information.

The present invention and existing medical science digital watermark relatively have following advantage:

Because the present invention is based on the multiple digital digital watermark of DFT conversion, and stronger resist geometric attacks ability and anti-conventional attack ability are arranged; Do not need artificial the choosing of area-of-interest of carrying out, improve the embedding speed of other watermark, and this invention has solved the problem that multi-watermarking embeds; The multi-watermarking that embeds is a kind of zero watermark, does not influence primitive medicine picture quality, aspect medical, has very high practical value.

Below from the explanation of theoretical foundation and test figure:

1) discrete Fourier transformation

Two-dimensional discrete Fourier direct transform (DFT) formula is following:

$F(u,v)=\frac{1}{\mathrm{MN}}\underset{x=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{y=0}{\overset{N-1}{\mathrm{\Σ}}}f(x,y)e^{-j2\mathrm{\π}(\frac{\mathrm{ux}}{M}+\frac{\mathrm{vy}}{N})}$

u＝0，1，Λ，M-1；v＝0，1，Λ，N-1；

Two-dimensional discrete Fourier inversion (IDFT) formula is following:

$f(x,y)=\frac{1}{\mathrm{MN}}\underset{u=0}{\overset{M-1}{\mathrm{\Σ}}}\underset{v=0}{\overset{N-1}{\mathrm{\Σ}}}F(u,v)e^{j2\mathrm{\π}(\frac{\mathrm{ux}}{M}+\frac{\mathrm{vy}}{N})}$

x＝0，1，Λ，M-1；y＝0，1，Λ，N-1

X wherein, y is the spatial domain sampled value; U, v are the frequency field sampled value, and digital picture is represented with the pixel square formation usually, i.e. M=N

Can know that from top formula the coefficient symbols of DFT is relevant with the phase place of component.

2) choosing method of medical image vision principal character vector

The main cause of present most of medical image watermarking algorithm resist geometric attacks ability is: people are embedded in digital watermarking in pixel or the conversion coefficient, and the slight geometric transformation of medical image usually causes the bigger variation of having of pixel value or transform coefficient values.So just, can make the watermark of embedding just under attack very easily.If can find the visual feature vector of reflection image geometry characteristics, when little geometric transformation took place image, tangible sudden change can not take place in this visual feature of image value so.Hayes research shows that as far as characteristics of image, phase place is more important than amplitude.We find through observing a large amount of full figure DFT data (Low Medium Frequency); When a medical image is carried out common geometric transformation; Some variations possibly take place in the Low Medium Frequency coefficient magnitude, but its coefficient symbols remains unchanged basically, and we choose some experimental datas and see shown in the table 1.Be used as a test sectioning image (128x128) in the table 1.What the 1st row showed in the table is medical image type under attack, and the medical image that receives behind the conventional attack is seen Fig. 1 (b)-(d), and the medical image that receives behind the geometric attack is seen Fig. 2 (a)-(d).The 3rd is listed as the 6th row, and this is FF (1, the 1)-FF (1,5) that in the DFT matrix of coefficients, gets, altogether 5x2=10 Low Medium Frequency coefficient (here a plural number, regarding two coefficients of real part and imaginary part as).Wherein coefficient F (1,1) representes the DC component value of medical image.For conventional attack, these Low Medium Frequency coefficient values FF (1,1)-FF (1,5) remains unchanged and primitive medicine image value approximately equal basically; For geometric attack, the part coefficient has bigger variation, but we can find that medical image is when receiving geometric attack, and the size of part DFT Low Medium Frequency coefficient has taken place to change but its symbol does not change basically.We are Fourier coefficient (plural number is regarded real part and two coefficient values of imaginary part as here), on the occasion of and small incidental expenses " 1 " expression, negative value is with " 0 " expression; So for the primitive medicine image, the FF in the DFT matrix of coefficients (1,1)-FF (1; 5) coefficient, corresponding coefficient symbols sequence is: " 1100001111 ", see that the 7th of table 1 is listed as; Observing these row can find; No matter conventional attack still is this symbol sebolic addressing of geometric attack keeps similar with the primitive medicine image energy, with primitive medicine image normalization related coefficient all big (seeing the 8th row), (having got 5 DFT coefficient symbols here for the purpose of convenient).

Prove that for further the DFT conversion coefficient symbol sebolic addressing of full figure is a vision key character that belongs to this figure,, see Fig. 3 (a)-(g) again different test patterns; Carry out full figure DFT conversion; Obtain corresponding DFT coefficient FF (1,1)-FF (4,4); And obtain the related coefficient with the symbol sebolic addressing of former figure, result of calculation is as shown in table 2.Can find out that from table 2 between the different medical images, it is bigger that symbol sebolic addressing differs, the degree of correlation is less, less than 0.5.

This explains that more the symbol sebolic addressing of DFT coefficient can reflect the main visual signature of this medical image.After watermarking images received conventional attack and geometric attack to a certain degree, this vector was constant basically, and this also meets the DFT ability that " very strong extraction characteristics of image arranged ".

Table 1 image full figure DFT conversion Low Medium Frequency part coefficient and receive different the attack after changing value

^*The 1.00e+003 of DFT conversion coefficient unit

The related coefficient of the different medical image proper vectors of table 2 (vector length 32bit)

	Pa	Pb	Pc	Pd	Pe	Pf	Pg
								Pa	1.00	0.38	0.25	-0.18	0.12	-0.26	0.00
Pb	0.38	1.00	0.38	-0.11	-0.12	0.14	-0.13
								Pc	0.25	0.38	1.00	-0.01	-0.25	0.24	0.13
Pd	-0.18	-0.11	-0.01	1.00	0.25	0.09	0.27
								Pe	0.12	-0.12	-0.25	0.25	1.00	-0.01	0.38
Pf	-0.26	0.14	0.24	0.09	-0.01	1.00	0.26
								Pg	0.00	-0.13	0.13	0.27	0.38	0.26	1.00

3) position of watermark embedding and the length of disposable embedding

According to human visual system (HVS), the Low Medium Frequency signal is bigger to people's visual impact, is representing the principal character of medical image.Therefore the visual feature vector of selected medical image is the symbol of Low Medium Frequency coefficient; It is relevant with the robustness of the quantity of information of the size of the primitive medicine image that carries out full figure DFT conversion and disposable embedding and requirement that the number of Low Medium Frequency coefficient is selected; The L value is more little; The quantity of information of disposable embedding is few more, but robustness is high more.In the test of back, the length of choosing L is 32.

In sum; Through analysis to the overall DFT coefficient of medical image; Utilize the symbol sebolic addressing of DFT Low Medium Frequency coefficient to obtain a kind of method of proper vector of a resist geometric attacks obtaining medical image, utilize this proper vector and Hash function, " third party " notion to realize in medical image, embedding the method for many watermarks.Through experiment showed, that this method has realized the embedding of many watermarks, and the embedding of watermark do not influence the content of medical image, and robustness is preferably arranged.

Description of drawings

Fig. 1 (a) is the primitive medicine image.

Fig. 1 (b) is the image that disturbs through Gauss.

Fig. 1 (c) is the image of attacking through JPEG.

Fig. 1 (d) is the image through medium filtering.

Fig. 2 (a) is the image through rotational transform.

Fig. 2 (b) is the image through convergent-divergent 2.0.

Fig. 2 (c) is the image through convergent-divergent 0.5.

Fig. 2 (d) is the image through vertical moving.

Fig. 3 (a) is standardized test chart MRI_1.

Fig. 3 (b) is standardized test chart MRI_2.

Fig. 3 (c) is standardized test chart MRI_3.

Fig. 3 (d) is standardized test chart Engine.

Fig. 3 (e) is standardized test chart Head.

Fig. 3 (f) is standardized test chart Teddy bear.

Fig. 3 (g) is standardized test chart Mri_1back.

The watermarking images of Fig. 4 (a) when not disturbing.

The watermark detection of Fig. 4 (b) when not disturbing.

Watermarking images when Fig. 5 (a) has Gauss to disturb (Gauss's interference strength is 3%).

Watermark detection when Fig. 5 (b) has Gauss to disturb.

Watermarking images (compression quality is 4%) after Fig. 6 (a) JPEG compression.

Watermark detection after Fig. 6 (b) JPEG compression.

Watermarking images behind Fig. 7 (a) medium filtering (through 20 filtering of [3,3]).

Watermark detection behind Fig. 7 (b) medium filtering.

Watermarking images behind Fig. 8 (a) rotation 20 degree.

Watermark detection behind Fig. 8 (b) rotation 20 degree.

Fig. 9 (a) zoom factor is 4.0 watermarking images.

Fig. 9 (b) zoom factor is 4.0 watermark detection.

Figure 10 (a) zoom factor is 0.5 watermarking images.

Figure 10 (b) zoom factor is 0.5 watermark detection.

Figure 11 (a) moves the image after 3%.(horizontal left)

Figure 11 (b) moves the watermark detection after 3%.

Figure 12 (a) shears 6% watermarking images.

Figure 12 (b) shears 6% watermark detection.

Embodiment

Below in conjunction with accompanying drawing the present invention is described further and uses 1000 groups of independently binary pseudo-random (value is+1 or 0); Every group of sequence length is 32bit; In these 1000 groups of data; Appoint to extract three groups (selecting the 300th group, the 500th group, the 700th group here), as three watermark sequences that embed, promptly we to have embedded total length be the watermark sequence of 32x3=96bit.Testing used primitive medicine image, is that the image (128x128) of a section of a brain is seen Fig. 4 (a).If former figure be expressed as F (i, j), 1≤i≤128,1≤j≤128 wherein; Corresponding full figure DFT matrix of coefficients is that (i j), selects Low Medium Frequency coefficient Y (j) to FF; 1≤j≤L, the DC component of first value Y (1) representative image, from low to high frequency order is arranged then.Consider the capacity of robustness and disposable embed watermark, we select 4x4=16 plural coefficient of medium and low frequency to do proper vector (here a plural number, regarding two coefficients of real part and imaginary part as), then total 16x2=32 Low Medium Frequency coefficient, i.e. L=32.The many watermarks W that embeds is by k sub-watermark W ^k(j) form, the number k of this routine neutron watermark gets 3, and this lining watermark is designated as W ^k(j), 1≤j≤32,1≤k≤3; The DFT matrix of coefficients of choosing be FF (i, j), 1≤i≤4,1≤j≤4.Detect W through watermarking algorithm ^k' (j) after, again through calculating W ^k(j) and W ^k' (j) normalized correlation coefficient NC ^k(Normalized Cross Correlation) for the purpose of making things convenient for, representes three related coefficients corresponding with three watermarks that extract with NC1, NC2 and NC3, is used to judge whether that watermark embeds.

Fig. 4 (a) is the watermarking images that does not add when disturbing;

Fig. 4 (b) does not add when disturbing, and the output of watermark detector can be seen NC1=1.00, NC2=1.00, and NC3=1.00 obviously detects the existence of watermark.

Below we judge the anti-conventional attack ability and the resist geometric attacks ability robustness of this digital watermark method through concrete test.

Test the ability of the anti-conventional attack of this watermarking algorithm earlier.

(1) adds Gaussian noise

Use imnoise () function in watermarking images, to add gaussian noise.

Fig. 5 (a) is for the watermarking images when Gaussian noise intensity is 3%, and is visually very fuzzy;

The output of Fig. 5 (b) watermark detector can clearly detect the existence of watermark, NC1=1.00, NC2=1.00, NC3=1.00.

Table 3 is the anti-Gauss of watermark detection data when disturbing.Can see from experimental data, when Gaussian noise intensity when being 25%, watermarking images PSNR reduces to 0.13dB; At this moment detect watermark, related coefficient NC1=0.90, NC2=0.93; NC3=0.93 still can detect the existence of watermark. and this explanation adopts this invention that good anti-Gaussian noise ability is arranged.

The anti-Gaussian noise interfering data of table 3 watermark

Noise intensity (%)	1	3	5	10	15	20	25
								PSNR(dB)	12.43	7.94	6.01	3.25	1.85	0.84	0.13
NC1	1.00	1.00	1.00	1.00	0.96	0.90	0.90
								NC2	1.00	1.00	1.00	1.00	0.95	0.93	0.93
NC3	1.00	1.00	1.00	1.00	0.93	0.93	0.93

(2) JPEG processed compressed

Adopt image compression quality percentage watermarking images to be carried out the JPEG compression as parameter;

Fig. 6 (a) is that compression quality is 4% image, and blocking artifact has appearred in this figure;

Fig. 6 (b) is the response of watermark detector, NC1=1.00, and NC2=1.00, NC3=1.00, it is obvious to detect effect.

Table 4 is the test figure of the anti-JPEG of watermarking images.When compression quality is very poor, compression quality is 2% o'clock, still can record the existence of watermark, NC1=0.75, NC2=0.75, NC3=0.75.

The experimental data of the anti-JPEG compression of table 4 watermark

Compression quality (%)	2	4	8	10	20	40	60	80
									PSNR(dB)	16.32	17.61	19.99	20.98	23.04	25.06	26.52	29.27
NC1	0.75	1.00	1.00	0.88	1.00	1.00	1.00	1.00
									NC2	0.75	1.00	1.00	0.90	1.00	1.00	1.00	1.00
NC3	0.75	1.00	1.00	0.89	1.00	1.00	1.00	1.00

(3) medium filtering is handled

Fig. 7 (a) is that the medium filtering parameter is [3x3], and the filtering multiplicity is 20 medical image, and bluring has appearred in image;

Fig. 7 (b) is the response of watermark detector, NC1=0.83, and NC2=0.82, NC3=0.85, it is obvious to detect effect.

Table 5 is the anti-medium filtering ability of watermarking images, and it can be seen from the table, when the medium filtering parameter is [7x7], the filtering multiplicity is 20 o'clock, still can record the existence of watermark, NC1=0.75, NC2=0.75, NC3=0.75.

The anti-medium filtering experimental data of table 5 watermark

Watermark resist geometric attacks ability:

(1) rotational transform

Fig. 8 (a) is 20 ° of watermarking images rotations, the PSNR=12.38dB of watermarking images at this moment, and signal to noise ratio (S/N ratio) is very low;

Fig. 8 (b) is the watermarking images of detection, can obviously detect the NC1=0.89 that exists of watermark, NC2=0.87, NC3=0.88.

Table 6 is the anti-rotation of watermark challenge trial data.Can see in the table when watermarking images rotates 30 °, NC1=0.69, NC2=0.68, NC3=0.71 still can detect watermark and exist; The resist geometric attacks algorithm that people such as Pitas propose embeds watermark in the annulus of DFT amplitude spectrum, can only resist the rotation that is not more than 3 degree.

Experimental data is attacked in the anti-rotation of table 6 watermark

(2) scale transformation

Fig. 9 (a) is the watermarking images when zoom factor 4.0, at this moment center image big than former figure;

Fig. 9 (b) is a watermarking detecting results, can detect the existence of watermark, NC1=1.00, NC2=1.00, NC3=1.00.

Figure 10 (a) is 0.5 watermarking images for zoom factor, at this moment center image little a lot of than former figure;

Figure 10 (b) is a watermarking detecting results, can obviously detect the NC1=1.00 that exists of watermark, NC2=1.00, NC3=1.00.

Table 7 is watermark convergent-divergent challenge trial data, from table 8 can see when the watermarking images zoom factor little to 0.2 the time, related coefficient NC1=0.75, NC2=0.75, NC3=0.75 still can record watermark.The method of in DFT, inserting template of employings such as Pereira can only be resisted zoom factor and be not less than 0.65 convergent-divergent, explains that this invention has stronger nonshrink exoergic power.

Table 7 watermark convergent-divergent is attacked experimental data

Zoom factor	0.2	0.5	0.8	1.00	1.2	2.0	4.0
								NC1	0.75	1.00	0.94	1.00	1.00	1.00	1.00
NC2	0.75	1.00	0.95	1.00	1.00	1.00	1.00
								NC3	0.75	1.00	0.94	1.00	1.00	1.00	1.00

(3) translation transformation

Figure 11 (a) is move to left 3% situation of image level, PSNR=12.28dB at this moment, and signal to noise ratio (S/N ratio) is very low;

Figure 11 (b) is watermark detector output, can obviously detect the NC1=0.94 that exists of watermark, NC2=0.93, NC3=0.94.

Table 8 is the anti-translation challenge trial of watermark data.From table, learn, still can detect the existence of watermark, so this digital watermarking has stronger anti-translation capability when level or vertical moving 10%.

Experimental data is attacked in the anti-translation of table 8 watermark

(4) shear test

Figure 12 (a) is for to shear 6% situation to watermarking images by Y direction, and at this moment the top has been sheared a part with respect to the primitive medicine image;

Figure 12 (b) is its watermark detection situation, can obviously detect the existence of watermark, NC1=0.87, NC2=0.90, NC3=0.87.

Table 9 is watermark cut-through resistance test data, and test figure can learn that this algorithm has certain anti-shear ability from table.

The anti-shearing attack experimental data of table 9 watermark (shearing) by Y direction

Through above description of test, the embedding grammar of this watermark has stronger anti-conventional attack ability and geometric attack ability, and the embedding of watermark do not influence the value of medical image, is a kind of zero watermark.

Claims (1)

1. but medical image multi-watermarking method based on the DFT resist geometric attacks; It is characterized in that: based on the extraction of the proper vector of overall DFT and resist geometric attacks; And the Hash function characteristic in digital watermark, the cryptography and " third party " notion combined; Realized the embedding of multiple digital watermarking in medical image, this method is divided into two parts, amounts to four steps:

First is that multi-watermarking embeds: through the embedding operation to multi-watermarking, obtain corresponding two-valued function sequence Key ^k(j);

1) the primitive medicine image is carried out overall DFT, in conversion coefficient, utilize the symbol sebolic addressing of Low Medium Frequency coefficient to obtain the vectorial V (j) of the resist geometric attacks of this figure;

2) utilize Hash function and the multi-watermarking W that will embed ^k(j), k=0,1,2 ..., n; Obtain two-valued function sequence Key ^k(j), ${\mathrm{Key}}^k\left(j\right)=V\left(j\right)\⊕W^k\left(j\right);$

Preserve Key ^k(j), to use when extracting watermark below, through Key ^k(j) apply for to the third party as key, to obtain the entitlement and the right to use to the primitive medicine image;

Second portion is that multi-watermarking extracts: through two-valued function sequence Key ^k(j) and the proper vector V ' of the resist geometric attacks of medical image to be measured (j), extract multi-watermarking W ^k' (j);

3) medical image to be measured is carried out overall DFT; In conversion coefficient, the visual feature vector V ' that goes out testing image according to the symbol extraction of Low Medium Frequency coefficient (j);

4) utilize Hash function character and have third-party Key ^k(j), extract watermark, $W^{k,}\left(j\right)={\mathrm{Key}}^k\left(j\right)\⊕V^{,}\left(j\right);$

With W ^k(j) and W ^k' (j) carry out normalized correlation coefficient calculating, confirm the entitlement of medical image.

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