CN111224771B - Management coding encryption and decryption method based on principal component analysis and Henon mapping - Google Patents

Management coding encryption and decryption method based on principal component analysis and Henon mapping Download PDF

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CN111224771B
CN111224771B CN202010033135.3A CN202010033135A CN111224771B CN 111224771 B CN111224771 B CN 111224771B CN 202010033135 A CN202010033135 A CN 202010033135A CN 111224771 B CN111224771 B CN 111224771B
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code
image
principal component
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CN111224771A (en
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金梅
张子豪
张少阔
李媛媛
赵伟
孟金岭
张勇
郎梦园
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Yanshan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/001—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using chaotic signals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
    • G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
    • G06K19/06009—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking
    • G06K19/06037—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking multi-dimensional coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/60—Digital content management, e.g. content distribution
    • H04L2209/608—Watermarking

Abstract

The invention discloses a management coding encryption and decryption method based on Principal Component Analysis and Henon mapping, and provides a QR code encryption method based on Principal Component Analysis (PCA) and chaos encryption combination on the basis of the original chaos encryption and decryption method. The conclusion that the encryption effect is good is obtained by evaluating the correlation between adjacent pixels and the information entropy.

Description

Management coding encryption and decryption method based on principal component analysis and Henon mapping
Technical Field
The invention belongs to the technical field of coding, and particularly relates to a management coding encryption and decryption method based on principal component analysis and Henon mapping.
Background
In recent years, the holding capacity of electric automobiles in China is rapidly increased, and the retired scale of the power battery in 2020 is expected to break through the 10GWH major line according to the calculation of 3-5 years of the service life of the power battery. The retired power battery can not meet the performance requirements of the retired power battery in the application field of electric automobiles, but can still play the role of the retired power battery in other occasions. Therefore, a traceability management system for the full life cycle of the power battery is needed to manage large-scale retired power batteries, and comprises several stages of power battery material planning, power battery recycling, power battery warehousing, power battery commissioning monitoring maintenance, power battery monitoring maintenance and power battery disposal, wherein in the power battery warehousing stage, each power battery needs to identify the identity information of the power battery by using a two-dimensional code with battery record information, so that scientific and fine management of the battery using process is realized.
The QR code is widely spread and applied as a carrier of information transmission, and has the advantages of high identification speed, omnibearing identification, high information capacity and the like, so that the QR code is widely used in the fields of mobile payment, warehouse logistics and the like. However, the QR code coding mode is simple and easy to identify, once the QR code carrying battery recovery information is intercepted, serious consequences of information leakage can be caused, the existing QR code encryption method is difficult to ensure the safety of the power battery during the life cycle backtracking, and the information safety of the power battery is protected by improving the QR code encryption method.
At present, the common QR code encryption technology used in engineering is a DES encryption method, the encryption method encrypts information content carried by a QR code, and then encrypted information is used for making the QR code, so that the content obtained after a user scans the code by using a mobile terminal is still encrypted content which is not easy to read, and the DES key has small space and lower safety, and cannot effectively resist attacks. The other common encryption technology is a chaotic encryption method, the encryption method directly encrypts the QR code image and is easier to read after decryption, but the method has the defects that the single chaotic system has low security, the image scrambling and diffusion cannot resist the attack of selecting plaintext, the encryption efficiency is low, and the like.
Disclosure of Invention
The invention aims to protect the information security of a power battery in a traceability management system of the whole battery life cycle, and combines a digital watermarking technology and a chaotic sequence encryption technology to form a more perfect and effective encryption method, thereby solving the problem of low QR code security.
The invention overcomes the defects in the prior art and provides a management coding encryption and decryption method based on principal component analysis and Henon mapping. The QR code encryption method based on Principal Component Analysis (PCA) and chaotic encryption combination is provided on the basis of the original chaotic encryption method, the QR code containing sensitive information is hidden into the QR code containing meaningless information through Principal Component Analysis, and a two-dimensional chaotic sequence generated through Henon mapping encrypts a generated image.
In order to solve the technical problems and achieve the purpose of the invention, the invention is realized by the following technical scheme:
a management coding encryption and decryption method based on principal component analysis and Henon mapping comprises the following steps:
s1, generating a QR code image by using the recovery information of the power battery in the traceability management system of the full life cycle of the power battery according to a QR code system code, and converting the QR code image into a matrix N only containing (0, 1); (ii) a
S2, randomly generating meaningless information with the character length and the like, coding the meaningless information according to a QR code system, generating a QR code image containing the meaningless information, and converting the QR code image into a matrix M only containing (0, 1); (ii) a
S3, carrying out spatial scrambling on the matrix N by using an Arnold algorithm to obtain a scrambled matrix P;
s4, carrying out blocking processing on the matrix M, carrying out principal component analysis on the processed image, and combining the analysis results to obtain a principal component matrix T;
and S5, embedding the matrix P into the coefficient T by using an additive embedding principle, selecting the optimal embedding strength coefficient for embedding operation, and then obtaining an embedded matrix Q by using the principal component analysis inverse transformation.
S6, determining four initial parameters of Henon mapping, and obtaining a two-dimensional chaotic matrix R with the same size as the matrix Q by using the Henon mapping.
And S7, adding the matrix R and the matrix Q to obtain an encrypted QR code matrix S, and ending the encryption process.
The decryption process comprises the following steps:
p1, inputting four initial parameters of a Henon mapping matrix to obtain an encryption matrix R, and carrying out subtraction operation on the matrix R and a matrix S to obtain a matrix Q;
p2, separating the pre-embedding matrix P by using principal component analysis on the matrix Q;
and P3, inputting Arnold algorithm parameters, recovering the original QR code matrix N by using the inversion solving principle of the algorithm, and ending the decryption process.
Preferably, the optimal embedding strength factor is based on:
T′＝T+aP
wherein a is the embedding strength, T is the principal component, P is the matrix to be embedded, and T' is the matrix after embedding, when a is too large, the robustness of the digital watermarking algorithm can be increased, and the hiding effect can be reduced, so the optimal embedding strength coefficient can be obtained through experimental screening.
Preferably, the encrypted QR code matrix S can change the image shape by the transmission transformation to make the image more confusing.
Due to the adoption of the technical scheme, compared with the prior art, the management coding encryption and decryption method based on principal component analysis and Henon mapping has the following beneficial effects:
the key space of the method expands the key space of digital watermark encryption besides the key space of the basic two-dimensional chaotic matrix, thereby effectively increasing the complexity of the encryption method and increasing the difficulty of brute force cracking. In addition, in order to embed the QR code containing sensitive information into the QR code containing invalid information, the principal coefficient of the QR code image containing sensitive information is extracted by using a PCA method, different from other frequency domain transformations, the coefficient extracted by PCA not only comprises high-frequency components but also low-frequency components, and the QR code image structure is not damaged during decoding and restoring, and the image detail can also be more contained. The robustness of the whole encryption method is improved.
Drawings
FIG. 1 is an encryption flow diagram of the management code encryption and decryption method based on principal component analysis and Henon mapping according to the present invention;
FIG. 2 is a flow chart of the decryption method of the present invention;
FIG. 3 is a QR code graph containing sensitive information according to the present invention;
FIG. 4 is a QR code map containing meaningless information according to the present invention;
FIG. 5 is a diagram of a QR code after scrambling in accordance with the present invention;
FIG. 6 is a graph of a QR code after embedding in accordance with the present invention;
FIG. 7 is a diagram of a QR code after encryption in accordance with the present invention;
figure 8 is a diagram of encrypted QR codes of different shapes generated by the present invention.
Detailed Description
The invention will be described in more detail with reference to the following detailed description and accompanying drawings:
the embodiment of the invention carries out simulation experiments on a computer with an 8.0G memory, a 64-bit operating system, an lntel CORE i5-8300H and a 2.3GHz processor, and selects a standard QR code binary image with the size of 100 multiplied by 100 from the QR code containing power battery recycling information.
A management coding encryption and decryption method based on principal component analysis and Henon mapping is disclosed, as shown in figure 1, the encryption process comprises the following steps:
firstly, in a battery quality inspection link in a power battery storage stage in a traceability management system of a full life cycle of a power battery, information such as the date of entry of a retired power battery, the battery state and the like is coded according to a QR code system to generate a QR code image containing power battery recycling information, and meaningless information with the same character length is randomly generated and coded according to the QR code system to generate the QR code image containing the meaningless information.
Secondly, reading a pixel matrix N of a QR code binary image containing power battery recycling information100×100As shown in fig. 3, the pixel matrix of the QR code binary image containing meaningless information is M100×100As shown in fig. 4, the gray scale image indicates that the gray scale value of each pixel of the QR code is 0 or 1.
The third step is to carry out matrix N according to the Arold algorithm100×100Spatial scrambling is performed as in equation 1:
Figure BDA0002365062270000051
wherein a and b are scrambling parameters, N is iteration times, N is image width and height, and xn，ynAs original pixel position, xn+1，yn+1The scrambled image pixel locations.
The parameters a and b and the number of iterations n are determined as the key1, and in the experiment, a equals b equals 1, and n equals 500, so as to obtain the scrambled QR code image P, as shown in fig. 5.
For improving the operation efficiency, the matrix M is aligned100×100And (5) carrying out main component analysis in blocks, and merging after obtaining results. Wherein, according to formula 2:
Figure BDA0002365062270000052
wherein xi，jIn the form of a matrix of elements,
Figure BDA0002365062270000054
is mean value, SjIs the variance of the matrix components, Zi，jIs the standard deviation. To M100×100Normalizing to obtain a normalized matrix Z, and then obtaining a normalized matrix Z according to a formula 3:
Figure BDA0002365062270000053
wherein r isi，jIs Zi，jC is a correlation coefficient matrix.
Obtaining a correlation coefficient matrix C, and then according to a formula 4:
|C-λIp|＝0 4
wherein λ is a characteristic value, IpIs an identity matrix of p × p.
P characteristic values are obtained, and the obtained characteristic values are expressed by lambda1≥λ2≥，…，≥λpSorting more than or equal to 0 and forming a characteristic coefficient matrix U (e) by corresponding characteristic vectors1，e2，…，ep)TAnd finally according to formula 5:
Figure BDA0002365062270000061
wherein
Figure BDA0002365062270000062
Is the transpose of the characteristic coefficient matrix, Z is the standard deviation matrix, TiIs the main component.
And transforming into a principal component matrix T, wherein m is the number of the selected principal components.
The fourth step is according to equation 6:
T′＝T+aP 6
wherein a is embedding strength, T is a principal component matrix, P is a matrix to be embedded, and T' is an embedded matrix, when a is too large, the robustness of the digital watermarking algorithm is increased, and the hiding effect is reduced, so through experimental screening, a is selected to be 0.06, and the parameter is stored as a key 2. And finally, according to formula 7 and formula 8:
Figure BDA0002365062270000063
Figure BDA0002365062270000064
wherein
Figure BDA0002365062270000065
The inverse of the transpose of the characteristic coefficient matrix, T' being the embedded matrix, Z being the normalized matrix, xi，jIn the form of a matrix of elements,
Figure BDA0002365062270000066
is mean value, SjIs the variance of the matrix components, Zi，jIs the standard deviation.
And performing principal component inverse transformation on the matrix T' embedded with the QR code containing the power battery recovery information to generate a meaningless QR code matrix Q hiding the QR code containing the power battery recovery information, wherein the matrix Q is converted into a gray scale as shown in FIG. 6.
Generating a two-dimensional chaotic matrix according to the Henon mapping expression, as shown in formula 9:
Figure BDA0002365062270000067
where x (n), y (n) are current matrix elements, x (n +1), y (n +1) are matrix elements after iteration, n is iteration number, and a and b are Henon mapping coefficients.
In which the Henon-mapped state is represented by x0，y0And a and b are determined, and when a is more than 0 and less than 1.4 and b is more than 0.2 and less than or equal to 0.314, the system enters a chaotic state, and the generated sequence is difficult to predict. The initial values of the four parameters in this experiment were chosen as x0＝0，y0These four parameter values are stored as key3, where a is 0.3 and b is 0.314. The two-dimensional chaotic matrix generated at this time is R.
Then, the obtained matrix R and the matrix Q are added bitwise, the result of S ═ Q + R is the encrypted matrix, S is converted into a grayscale image as shown in fig. 7, and finally perspective transformation can be added to change the shape of the image as shown in fig. 8, and the encryption process is ended.
The decryption process includes the following steps, as shown in fig. 2:
in the first step, an encrypted image is obtained through perspective transformation inverse transformation, a two-dimensional chaotic matrix R which is the same as that in the encryption process is generated through a key3, subtraction operation Q is carried out on an encrypted gray matrix S to be S-R, and an embedded matrix Q can be restored.
The second step is to perform principal component analysis on the matrix Q, the principal component analysis step is the same as the second step of the encryption process, and the key2 is input, and the formula is used
Figure BDA0002365062270000071
The QR code matrix P containing the recovery information of the power battery can be extracted. Wherein a is embedding strength, T is a main component, P is a matrix to be embedded, and T' is an embedded matrix.
The third step is to inverse transform the input key1 using the Arnold algorithm on the matrix P, as shown in equation 10:
Figure BDA0002365062270000072
wherein x (N), y (N) are the current matrix element positions, x (N +1), y (N +1) are the matrix element positions after iteration, a and b are scrambling parameters, N is the iteration times, and N is the image width and height.
And the obtained result is a QR code matrix containing the power battery recycling information, and the plaintext information before encryption can be obtained through scanning and identification. The decryption process ends.
In the present experiment, the encryption method is analyzed in two aspects, on one hand, the correlation between adjacent pixels is analyzed, and the correlation between adjacent pixels reflects the correlation degree of pixel values at adjacent positions of an image, and a good image encryption method should reduce the correlation between adjacent pixels and approach zero correlation as much as possible, and generally should analyze three aspects of horizontal, vertical and diagonal pixels of an image, as shown in formula 11:
Figure BDA0002365062270000081
where N is the random extraction of N pairs of adjacent pixel values, x, in a matrix of image pixel valuesi，yiIs the pixel point position, and c is the evaluation result. The experiment randomly abstracts 1000 to analyze the pixel values. The analytical results are shown in Table 1.
On the other hand, the information entropy is analyzed, and is a measure of the amount of information needed to remove uncertainty, i.e., the amount of information that an unknown event may contain. The larger the information entropy of the gray level image is, the more disordered the description information is, and the better the encryption effect is. The information entropy formula is shown in formula 12:
Figure BDA0002365062270000082
wherein P (x)i) Denotes that the random variable x is xiH (x) identification information entropy. The analytical results are shown in Table 2.
TABLE 1 correlation between adjacent pixels of original QR code and encrypted QR code
Figure BDA0002365062270000091
TABLE 2 correlation of adjacent pixels of original QR code and encrypted QR code
Image of a person Original QR code Encrypted QR (quick response) code
Entropy of information 0.9987 1.2633
According to experimental results, on one hand, the correlation of pixel values of adjacent positions of the encrypted QR code in the horizontal direction, the vertical direction and the diagonal direction is smaller than that of the original QR code by more than one order of magnitude, which shows that the correlation of image contents after encryption is obviously reduced, and the encryption effect is good. On the other hand, the information entropy of the encrypted QR code is larger than that of the original QR code, so that the encrypted image is more disordered and more difficult to identify, and the encryption effect is good.
The QR code containing power battery recycling information is embedded into the QR code containing meaningless information by using a digital watermarking technology based on principal component analysis, and a two-dimensional chaotic matrix generated by Henon mapping is used for carrying out secondary encryption on the embedded image. The evaluation algorithm proves that the encryption effect of the invention is good, and the problems of information leakage and the like can be effectively prevented under the traceability management system of the whole life cycle of the power battery.
The above-mentioned embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims (2)

1. A management coding encryption and decryption method based on principal component analysis and Henon mapping is characterized in that:
the encryption process comprises the following steps:
s1, generating a QR code image by using the recovery information of the power battery in the traceability management system of the full life cycle of the power battery according to a QR code system code, and converting the QR code image into a matrix N only containing (0, 1);
s2, randomly generating meaningless information with the character length and the like, coding the information according to a QR code system, generating a QR code image, and converting the QR code image into a matrix M only containing (0, 1);
s3, carrying out spatial scrambling on the matrix N by using an Arnold algorithm, generating a key1, storing the key in a database, and obtaining a scrambled matrix P;
matrix N is paired according to the Arold algorithm100×100Spatial scrambling is performed as in equation 1:
Figure FDA0002951176100000011
wherein a and b are scrambling parameters, N is iteration times, N is image width and height, and xn，ynAs original pixel position, xn+1，yn+1The pixel position of the scrambled image is obtained;
determining parameters a and b and the iteration number n as a key1, and taking a as b as 1 and n as 500;
s4, carrying out blocking processing on the matrix M, carrying out principal component analysis on the processed image, and combining the analysis results to obtain a principal component matrix T;
s5, embedding the matrix P into the coefficient T by using an addition embedding principle, selecting the optimal embedding strength coefficient for embedding, generating a key2, storing the key in a database, and performing inverse transformation on the embedding result by using principal component analysis to obtain an embedded matrix Q;
the optimal embedding strength coefficient is based on:
T′＝T+aP
wherein a is embedding strength, T is a principal component matrix, P is a matrix to be embedded, and T' is an embedded matrix, when a is too large, the robustness of the digital watermarking algorithm is increased, and the hiding effect is reduced, so that the optimal embedding strength coefficient a is 0.06 through experimental screening;
s6, determining four initial parameters of Henon mapping, generating a key3, storing the key in a database, and obtaining a two-dimensional chaotic matrix R with the same size as the matrix Q by using the Henon mapping;
generating a two-dimensional chaotic matrix according to the Henon mapping expression, as shown in formula 9:
Figure FDA0002951176100000021
wherein x (n), y (n) are current matrix elements, x (n +1), y (n +1) are matrix elements after iteration, n is iteration times, and a and b are Henon mapping coefficients;
in which the Henon-mapped state is represented by x0,y0A, b are determined by four parameters and are 0<a<1.4，0.2<When b is less than or equal to 0.314, the system enters a chaotic state, and the generated sequence is difficult to predict; the initial values of the four parameters are selected as x0＝0,y00, 0.3, 0.314, and these four parameter values are stored as key 3; the generated two-dimensional chaotic matrix is R;
s7, carrying out bitwise addition operation on the obtained matrix R and the matrix Q, wherein the result of S-Q + R is an encrypted matrix, converting S into a gray image, and finally adding perspective transformation to change the shape of the image, and ending the encryption process;
the decryption process comprises the following steps:
p1, reading a key3 from the database, inputting four initial parameters of a Henon mapping matrix to obtain an encryption matrix R, and carrying out subtraction operation on the matrix R and the matrix S to obtain a matrix Q;
p2, reading a key2 from the database, and separating a pre-embedding matrix P by using principal component analysis on the matrix Q;
p3, reading a key1 from the database, inputting Arnold algorithm parameters, recovering an original QR code matrix N by using the inversion solving principle of the algorithm, and ending the decryption process.
2. The method for managed coding encryption and decryption based on principal component analysis and Henon mapping according to claim 1, wherein: the encrypted QR code matrix S can change the shape of an image by transmission transformation to make the image more confusing.
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