CN105719224A - Biological characteristic image encryption method based on dual-tree complex wavelet transformation - Google Patents
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Abstract
The invention discloses a biological characteristic image encryption method based on dual-tree complex wavelet transformation. The encryption method comprises steps of firstly during an encryption process, converting a biological characteristic image to a coefficient matrix by use of the dual-tree complex wavelet transformation; then measuring the coefficient matrix and a pseudo random gauss matrix so as to obtain a measurement value matrix, and scrambling positions of elements of the measurement value matrix; during a decryption process, inputting a correct encryption key, carrying out reverse Fibonacci scrambling on a cryptograph and reconstructing a coefficient matrix by use of an orthogonal matching pursuit algorithm; and by use of the reversed dual-tree complex wavelet transformation, converting the coefficient matrix and finally reconstructing a biological characteristic image. The beneficial effects are that the cryptograph image is reflected in a quite even noise manner, so any related information in an original text image cannot be obtained from the cryptograph image at all; detail information can be well processed and restored; and the encryption method is favorable in encryption and decryption effects and highly safe.
Description
Technical field
The present invention relates to a kind of biometric image encryption method in conjunction with compressed sensing, specifically the encryption method of the biometric image of a kind of compressed sensing based on dual tree complex wavelet transform.
Background technology
Biometric image is different from general digital picture, in biometric image under cover individual privacy and biometric identity information.Once stolen, due to personal biology characteristics Limited Number, it is impossible to change and cancel.The place the same with digital picture, biometric image has and accounts for the feature that memory space is big, can affect efficiency of transmission, and this is also that it needs major reason to be compressed.Therefore combining encryption and compress technique, is possible not only to improve transfer rate, reduces memory space, and ensured the safety of biometric image.
Traditional encryption method is based on grouping algorithm, and such as DES and AES, image, based on computation complexity, is carried out encryption by turn by safety, and speed is slow, it is impossible to meet nowadays this efficiently social demand.The algorithm of existing image encryption, is mainly based upon spatial domain and the scramble of frequency domain, diffusion, xor operation.In the encryption method of traditional combining image compression, it is common that by separately performed to ciphering process and compression process, such way speed is slow, computationally intensive, also cannot meet the demand of real-time.Combine compression and refer to introducing image encryption in compression process with the method encrypted, it is ensured that high efficiency, enhance safety.
At present, common Standard of image compression includes JPEG, JPEG2000, they are utilized respectively discrete cosine transform (DiscreteCosineTransform, DCT), wavelet transform (DiscreteWaveletTransform, DWT) image being transformed from a spatial domain to frequency domain, this can cause that the compression process of image is lossy compression method.The appearance of compressed sensing, has broken traditional sampling theory, and it utilizes less sample rate to obtain sample of signal, reconstructs original signal thus undistorted.Therefore, increasing researcheres study the process of compressed sensing, are dissolved in the process of compressed sensing by image encryption, it is possible to ensure safety of image, save the calculating time, also improve the quality of reconstructed image.This method has higher safety, stability and high efficiency, but it adopts common Fourier transformation, lost the detailed information of a part so that reconstructed image is second-rate in conversion process.
Common DWT conversion, only has level, vertical, diagonally opposed selection, and its directional sensitivity is more weak.If there is only small skew in the signal of input, may result in the Energy distribution of wavelet coefficient and changing a lot, therefore DWT conversion does not possess the feature of translation invariance.Adopt dual tree complex wavelet transform (Dual-treeComplexWaveletTransform, DT-CWT) shortcoming overcoming these two aspects, this conversion is with two parallel wavelet tree structures, and provides the selection of six direction, and the multiple dimensioned down-sampling that eliminates is to keep approximate translation invariant.Generally being widely used in fields such as image denoising, image co-registration, image texture extraction and classification, the process to image detail of these fields, the location to characteristics of image and tracer request are higher.But, biometric image minutia is enriched, it is possible to adopt the compressed sensing based on DT-CWT, and the biometric image details reconstructed retains comparatively complete, the performance that the impact of these minutias identifies.
Summary of the invention
For solving above technical deficiency, the invention provides the biometric image encryption method of a kind of compressed sensing based on dual tree complex wavelet transform, it is effective that it is encrypted and deciphers, and safety is high.
The biometric image encryption method of a kind of compressed sensing based on dual tree complex wavelet transform of the present invention, comprises the following steps:
A., in ciphering process, dual tree complex wavelet transform is adopted to convert biometric image to coefficient matrix；
B. using pseudorandom Gaussian matrix as the calculation matrix in compressed sensing, random seed is generated calculation matrix as seed key, pseudorandom Gaussian matrix and seed key；
C. by the coefficient matrix after conversion in step a and the step b pseudorandom Gaussian matrix generated measurement, measured value matrix is obtained；In conjunction with dimensional Logistic chaos system and Fibonacci scrambling algorithm, the position of this measured value matrix element of scramble, now the value of the initial parameter of Logistic chaos system is as encryption key, and the measured value matrix after scramble is as ciphertext；
D. in decrypting process, the encryption key in input step c, ciphertext is carried out inverted-F ibonacci scramble, and obtains seed key to generate pseudorandom Gauss measurement matrix, adopt orthogonal matching pursuit algorithm to reconstruct coefficient matrix；
E. adopt anti-dual tree complex wavelet transform, the coefficient matrix in step d is converted, finally reconstruct biometric image.
Above-mentioned in step a, dual tree complex wavelet transform adopts two parallel filtering tree exploded view pictures, and dual tree complex wavelet transform selects 4 layers of mode decomposed, and ground floor adopts the nearly orthogonal filter equalizer of 13/19, all the other three layers are decomposed the q-shift bank of filters of employing 14 taps and are decomposed；Each decomposition layer can produce this ± 15 °, ± 45 °, the high frequency complex coefficient subband of ± 75 ° of six directions.
Above-mentioned in step a, biometric image is broken down into the low frequency subgraph picture of real number and the high frequency subimage of the plural number on a series of six direction；Utilize multi-direction exploded view picture, it is ensured that the approximate translation invariance of image, and the Energy distribution of dual tree complex wavelet transform coefficient is almost constant, unaffected to guarantee the biometric image minutia extracted.
Above-mentioned in step e, adopt anti-dual tree complex wavelet transform, the high frequency subimage of the plural number on the low frequency subgraph picture of real number and a series of six direction in input coefficient matrix, ground floor selects the nearly orthogonal wave filter reconstruct of 13/19, all the other three layers select the q-shift bank of filters of 14 taps, finally reconstruct biometric image.
The invention has the beneficial effects as follows: the ciphertext graph picture of the present invention embodies with the form of a kind of comparatively uniform noise, cannot obtain relevant information any to original document image from ciphertext graph picture at all.Picture quality after deciphering is high, the situation of information dropout almost without, for the image that this kind of minutia of biometric image is obvious and important, can process well and recover detailed information, image after cannot picking out plaintext and deciphering, that encrypts and decipher is effective, and safety is high.
Accompanying drawing explanation
Fig. 1 is raw biometric image of the invention process (fingerprint and iris).
Fig. 2 is the ciphertext biometric image (fingerprint and iris) after Fig. 1 adopts the method encryption of the present invention.
Fig. 3 is the biometric image (fingerprint and iris) after Fig. 1 adopts the method for the present invention to be deciphered by Fig. 2 after inputting correct key.
Fig. 4 is the biometric image (fingerprint and iris) after being deciphered by Fig. 2 after Fig. 1 adopts the method input error key of the present invention.
Fig. 5 is the biometric image (fingerprint and iris) of deciphering after Fig. 1 converts based on DWT.
Detailed description of the invention
Below in conjunction with accompanying drawing, the present invention is done further detailed description:
It is an object of the invention to provide the encryption method of the biometric image of a kind of compressed sensing based on dual tree complex wavelet transform, after this encryption, image has the feature of approaches uniformity noise, relevant information any to original document image cannot be obtained from ciphertext graph picture at all, there is higher safety.Image after deciphering reconstruct, it is possible to recovering original document image preferably, minutia information dropout is minimum, has higher reconstitution.
The present invention comprises the following steps:
A. dual tree complex wavelet transform: first, in ciphering process, dual tree complex wavelet transform adopts two parallel filtering tree decomposing organism characteristic images.Selecting 4 layers of mode decomposed, ground floor adopts the nearly orthogonal filter equalizer of 13/19, and all the other three layers decompose the q-shift bank of filters decomposition adopting 14 taps.± 15 ° can be produced at each decomposition layer, ± 45 °, the high frequency complex coefficient subband of ± 75 ° of these six directions, thus providing the selection on more direction, catch more biometric image minutia.Image is broken down into the low frequency subgraph picture of real number and the high frequency subimage of the plural number on a series of six direction.When pending image exists small skew, utilize multi-direction exploded view picture, it is ensured that the approximate translation invariance of image, and the Energy distribution of dual tree complex wavelet transform coefficient is almost constant, unaffected to guarantee the image detail feature extracted.Dual tree complex wavelet transform is with the structure of two parallel filtering trees, two-way complex wavelet transform, and a road generates the real part of conversion, and another road generates the imaginary part of conversion.Adopt orthogonal wavelet group, high-frequency sub-band provides the selection of more direction, better catches the minutia of image.Image after reconstruct can recover substantial amounts of detailed information, by calculating the PSNR value of reconstruct image, it can be determined that the quality of reconstructed image quality.For the Lena figure being sized to 256 × 256, the image PSNR value after its reconstruct is 43.4891dB, contrasts the image PSNR value that existing algorithm reconstructs and is significantly improved.
B. the generation of calculation matrix: using pseudorandom Gaussian matrix as the calculation matrix in compressed sensing, generates calculation matrix using random seed as seed key, pseudorandom Gaussian matrix and seed key；
C. scramble measured value matrix: by the coefficient matrix after conversion and calculation matrix measurement, obtain measured value matrix.In conjunction with dimensional Logistic chaos system and Fibonacci scrambling algorithm, the position of the measured value matrix element that scramble obtains, now the value of the initial parameter of Logistic chaos system is as the key of encryption, and the measured value matrix after scramble is as ciphertext.Adopting dimensional Logistic chaos system, the value of two initial parameters of input, and iteration to obtain chaos value 1000 times, this value as the seed producing pseudorandom Gaussian matrix, can utilize this seed to generate calculation matrix.
D. orthogonal matching pursuit restructing algorithm: in decrypting process, inputs correct key, and ciphertext is carried out inverted-F ibonacci scramble；And acquisition seed key is used for generating pseudorandom Gauss measurement matrix；Orthogonal matching pursuit algorithm is adopted to reconstruct coefficient matrix.In conjunction with Fibonacci scrambling algorithm and dimensional Logistic chaos system, the position of the measured value matrix element after the measurement of scramble compressed sensing.Logistic chaos system iteration is utilized to go out and the chaos sequence of measured value matrix element same number, and corresponding with measured value matrix element position.This chaos sequence is ranked up, and the position of the element in chaos sequence changes, and is simultaneously mapped to measured value entry of a matrix element position, thus reaching the purpose of scramble measured value matrix.Employing two dimensional Logistic chaos systems in this algorithm, therefore, the value having four initial parameters can as key, and the value of these parameters, once input error, all can cause the failure of deciphering.
E. anti-dual tree complex wavelet transform: the high frequency subimage of the plural number on the low frequency subgraph picture of in-real and a series of six direction, ground floor selects the nearly orthogonal wave filter reconstruct of 13/19, all the other three layers select the q-shift bank of filters of 14 taps, finally reconstruct image.
The computer simulation experiment of the present embodiment method is as follows:
The biometric image (fingerprint and iris) of emulation experiment is Fig. 1, and it is sized to 256 × 256 pixels.The Decomposition order of dual tree complex wavelet transform is 4.Ground floor adopts the nearly orthogonal filter equalizer of 13/19, including h0o, h1o, g0o, g1o wave filter, wherein h0o and g1o be sized to 13 × 1, its coefficient of g1o is-0.0018,0,0.0223,0.0469 ,-0.0482 ,-0.2969,0.5555 ,-0.2969 ,-0.0482,0.0469,0.0223,0 ,-0.0018；H1o and g0o is sized to 19 × 1, and its coefficient of g0o is 7.0626e-05, and 0 ,-0.0013 ,-0.0019,0.0072,0.0239,-0.0556 ,-0.0517,0.2998,0.5594,0.2998 ,-0.0517 ,-0.0556,0.0239,0.0072 ,-0.0019 ,-0.0013,0,7.0626e-05；And the absolute value of the filter coefficient of h0o and g1o, h1o and g0o is identical.All the other three layers decompose adopt 14 taps q-shift bank of filters decompose, q-shift bank of filters include 8 be sized to 14 × 1 wave filter, its coefficient of h0a is 0.0033,-0.0039,0.0347 ,-0.0389,-0.1172,0.2753,0.7561,0.5688,0.0119 ,-0.1067,0.0238,0.0170 ,-0.0054 ,-0.0046；Its coefficient of g0a is-0.0046 ,-0.0054,0.0170,0.0238 ,-0.1067,0.0119,0.5688,0.7561,0.2753 ,-0.1172 ,-0.0389, and-0.0054,0.0170,0.0238,0.0170 ,-0.0054 ,-0.0046；Wherein h0a and g0b, g0a and h0b, h1a and g1b, g1a and h1b are identical, use anisotropic filter to convert HFS, and low frequency part does not convert.
Initializing Fibonacci scramble number of times is 10 times, and the value of the parameter of Logistic chaos system is 0.10001 respectively, 3.888,0.5,3.7, and using these numerical value as key.Utilizing these keys can affect the line number of pseudorandom Gaussian matrix and the position of scramble measured value matrix element, the measured value matrix after display encryption, as in figure 2 it is shown, image is compressed, size becomes 124 × 256.In decrypting process, input the image after correct key value deciphering, as shown in Figure 3, measured value matrix can be carried out inverted-F ibonacci scramble and be produced as random Gaussian matrix, recycling OMP algorithm reconstructs coefficient matrix, the coefficient matrix reconstructed is resolved into the plural high frequency subimage on low frequency subgraph picture and six direction, recycle anti-DT-CWT conversion and ciphertext is recovered image, anti-DT-CWT conversion reconstruct image, ground floor adopts the nearly orthogonal filter equalizer of 13/19, and all the other three layers decompose the q-shift bank of filters decomposition adopting 14 taps.Fig. 4 is the reconstruct image obtained after the secret key decryption of mistake, as indicated, image has the feature of approaches uniformity noise after encryption, cannot obtain relevant information any to original document image at all, have higher safety from ciphertext graph picture.Relative to DT-CWT, the reconstructed image quality based on DWT conversion is poor, there is mosquito noise.
Table 1 is Y-PSNR and the error rate of the decrypted image of the Fig. 1 selecting this method and the biometric image (fingerprint, iris) based on DWT conversion process to obtain:
Coming as can be seen from Table 1, picture quality fingerprint image deciphering reconstructed based on the DT-CWT encryption method converted to exceed 17.89dB than based on the DWT image converted, and deciphers the image reconstructed and original image lower error rate 16.59%.Under similarity condition, the mass ratio of the iris image reconstructed to exceed 19.49dB based on the DWT image converted, and error rate decreases 1.8%.Thus solve the image distortion problems caused when image is decrypted by existing encryption method.As can be seen from Figure 5 based on the distorted image condition of DWT conversion.
The above is only the preferred implementation of this patent; it should be pointed out that, for those skilled in the art, under the premise without departing from the art of this patent principle; can also making some improvement and replacement, these improve and replace the protection domain that also should be regarded as this patent.
Claims (4)
1. the biometric image encryption method based on the compressed sensing of dual tree complex wavelet transform, it is characterised in that comprise the following steps:
A., in ciphering process, dual tree complex wavelet transform is adopted to convert biometric image to coefficient matrix；
B. using pseudorandom Gaussian matrix as the calculation matrix in compressed sensing, random seed is generated calculation matrix as seed key, pseudorandom Gaussian matrix and seed key；
C. by the coefficient matrix after conversion in step a and the step b pseudorandom Gaussian matrix generated measurement, measured value matrix is obtained；In conjunction with dimensional Logistic chaos system and Fibonacci scrambling algorithm, the position of this measured value matrix element of scramble, now the value of the initial parameter of Logistic chaos system is as encryption key, and the measured value matrix after scramble is as ciphertext；
D. in decrypting process, the encryption key in input step c, ciphertext is carried out inverted-F ibonacci scramble, and obtains seed key to generate pseudorandom Gauss measurement matrix, adopt orthogonal matching pursuit algorithm to reconstruct coefficient matrix；
E. adopt anti-dual tree complex wavelet transform, the coefficient matrix in step d is converted, finally reconstruct biometric image.
2. according to claim 1 based on the biometric image encryption method of the compressed sensing of dual tree complex wavelet transform, it is characterized in that: in step a, dual tree complex wavelet transform adopts two parallel filtering tree exploded view pictures, and dual tree complex wavelet transform selects 4 layers of mode decomposed, ground floor adopts the nearly orthogonal filter equalizer of 13/19, and all the other three layers decompose the q-shift bank of filters decomposition adopting 14 taps；Each decomposition layer can produce this ± 15 °, ± 45 °, the high frequency complex coefficient subband of ± 75 ° of six directions.
3. according to claim 1 based on the biometric image encryption method of the compressed sensing of dual tree complex wavelet transform, it is characterized in that: in step a, biometric image is broken down into the low frequency subgraph picture of real number and the high frequency subimage of the plural number on a series of six direction；Utilize multi-direction exploded view picture, it is ensured that the approximate translation invariance of image, and the Energy distribution of dual tree complex wavelet transform coefficient is almost constant, unaffected to guarantee the biometric image minutia extracted.
4. according to claim 1 based on the biometric image encryption method of the compressed sensing of dual tree complex wavelet transform, it is characterized in that: in step e, adopt anti-dual tree complex wavelet transform, the high frequency subimage of the plural number on the low frequency subgraph picture of real number and a series of six direction in input coefficient matrix, ground floor selects the nearly orthogonal wave filter reconstruct of 13/19, all the other three layers select the q-shift bank of filters of 14 taps, finally reconstruct biometric image.
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| CN109977807A (en) * | 2019-03-11 | 2019-07-05 | 首都师范大学 | Skin detection guard method and system based on complex matrix |
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