CN111046997B - Image security code generation method based on anti-counterfeiting encryption and visual identification - Google Patents
Image security code generation method based on anti-counterfeiting encryption and visual identification Download PDFInfo
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Abstract
The invention provides an image security code generation method based on anti-counterfeiting encryption and visual identification, which organically combines an encrypted security code and an image identification code through special encryption processing and data coding processing to correspondingly obtain the image security code.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to an image security code generation method based on anti-counterfeiting encryption and visual identification.
Background
With the continuous development of the intelligent identification technology of the mobile terminal, different types of intelligent identification codes appear in the prior art, and a user can acquire data information contained in the intelligent identification codes by scanning the intelligent identification codes through the mobile terminal. At present, an intelligent identification code mainly comprises two types, namely an image identification code and a Zhongan code serving as an encrypted security code, wherein the image identification code specifically comprises a stacked two-dimensional code, a QR two-dimensional code, a data matrix code, a compact matrix code, a square code and the like; the safety code is an information code with higher anti-counterfeiting security and error control capability by processing the original data information by adopting a special information compression technology, an information coding technology, an information encryption technology and the like. Because the intelligent identification code in the prior art does not have the characteristics of large capacity, large fault-tolerant rate, high anti-counterfeiting safety and convenience for wide application at the same time. The method combines the easy identification and the convenient wide application of the image identification code with the large capacity, the large fault tolerance rate and the high anti-counterfeiting performance of the Zhongan code, and can enable the intelligent identification to be used in various fields combining modern information technology and the Internet of things.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an image security code generation method based on anti-counterfeiting encryption and visual identification, which organically combines an encrypted security code and an image identification code through special encryption processing and data coding processing to correspondingly obtain the image security code.
The invention provides an image security code generation method based on anti-counterfeiting encryption and visual identification, which is characterized by comprising the following steps of:
the method comprises the following steps that S1, first characteristic information of a first object is obtained, and an encrypted security code is generated according to the first characteristic information;
s2, acquiring second characteristic information of a second object, and generating an image identification code according to the second characteristic information;
s3, acquiring fault tolerance rate information of the encrypted security code in a preset space, and generating an image identification reset code of the image identification code according to the fault tolerance rate information;
s4, filling processing related to the image identification reset code is carried out on the space where the encrypted security code is located, so that the image security code is obtained;
further, in the step S1, acquiring first characteristic information about the first object, and generating the encrypted security code according to the first characteristic information specifically includes,
a step S101 of performing a first information extraction process on at least one of space, image, and audio on the first object to obtain first object extraction information;
step S102, according to the first object extraction information, constructing a first feature matrix related to the first object, and performing matrix learning analysis processing on the first feature matrix to obtain the first feature information;
step S103, carrying out encryption conversion processing on the first characteristic information to obtain encrypted security characteristic information;
step S104, carrying out code conversion processing on the encrypted security feature information to obtain the encrypted security code;
further, in the step S101, performing a first information extraction process on at least one of space, image, and audio on the first object to obtain first object extraction information specifically includes,
step S1011 of performing first information extraction processing on at least one of a two-dimensional space, a three-dimensional space, an image color, an image texture, an image contour, a sound intensity distribution, and a sound frequency distribution on the first object to obtain first preliminary-order extraction information;
step S1012, performing error correction processing on a data structure and/or data noise on the first primary extraction information to obtain the first object extraction information;
alternatively, the first and second electrodes may be,
in the step S102, constructing a first feature matrix about the first object according to the first object extraction information, and performing matrix learning analysis processing on the first feature matrix to obtain the first feature information specifically includes,
step S1021, acquiring a plurality of characterization information of space dimension characteristics, image optical characteristics and sound audio characteristics of the first object from the first object extraction information;
step S1022, according to a preset feature arrangement condition, performing information arrangement processing on the plurality of characterization information to obtain the first feature matrix;
step S1023, carrying out matrix learning analysis processing on the first characteristic matrix through a preset security code neural network model to determine matrix evolution parameters of the first characteristic matrix so as to obtain first characteristic information;
further, in step S103, performing encryption conversion processing on the first feature information to obtain encrypted security feature information specifically includes
A step S1031 of performing information arrangement processing on the first feature information about a two-dimensional space or a three-dimensional space to obtain two-dimensional lattice distribution structure information or three-dimensional lattice distribution structure information about the first feature information;
step S1032, according to a preset encryption mode, carrying out lattice structure arrangement transformation on the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information so as to encrypt and convert the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information into the encrypted security feature information;
alternatively, the first and second electrodes may be,
in the step S104, performing transcoding processing on the encrypted security feature information to obtain the encrypted security code specifically includes,
step S1041, acquiring a data length, a data code repetition degree and a data code filling degree corresponding to the encryption security characteristic information, and optimizing a preset code conversion model according to the data length, the data code repetition degree and the data code filling degree;
step S1042, according to the optimized preset code conversion model, converting the encrypted security feature information into the encrypted security code;
further, in the step S2, acquiring second feature information on a second object, and generating an image recognition code according to the second feature information specifically includes,
step S201, acquiring a full-range image of the second object, wherein the full-range image is a background image at least including part of information of the second object;
step S202, carrying out color gamut characteristic value extraction processing on the background image to generate a spatial color gamut distribution lattice related to the background image;
step S203, generating two-dimensional code array value information about the second object as the second feature information according to the spatial color gamut distribution lattice;
step S204, generating the image identification code according to the two-dimensional code array value;
further, in the step S201, a full-range image about the second object is acquired, wherein the full-range image is specifically included by a background image including at least a part of information of the second object,
step S2011 of performing a photographing process on the second object to obtain a primary-order photographed image about the second object;
step S2012, judging whether the primary shot image has face information according to a preset face recognition algorithm;
step S2013, if the primary shot image does not have the face information, directly taking the primary shot image as a full-range image, and if the primary shot image has the face information, carrying out image corrosion processing on the primary shot image according to the face information to obtain a corresponding full-range image;
further, in the step S202, performing color gamut feature value extraction processing on the background image to generate a spatial color gamut distribution lattice with respect to the background image specifically includes,
step S2021 of performing color gamut feature extraction processing on the background image with respect to HSI to generate HSI component values with respect to hue H, saturation S, and lightness I of the background image;
step S2022, calculating a color gamut evaluation value corresponding to each pixel point in the background image according to the HIS component value;
step S2023, converting color gamut information corresponding to each pixel point in the background image into the spatial color gamut distribution lattice on the two-dimensional plane according to the color gamut evaluation value corresponding to each pixel point;
further, in the step S203, generating two-dimensional code array value information about the second object according to the spatial color gamut distribution lattice to specifically include as the second feature information,
step S2031, carrying out digital conversion processing on each dot matrix of the spatial color gamut distribution dot matrix to obtain a corresponding dot matrix digital representation value;
step S2032, combining the digital pointer values of the dot matrix into the two-dimensional code array value information according to the arrangement state of all the dot matrixes in the spatial color gamut distribution dot matrix;
alternatively, the first and second electrodes may be,
in step S204, the generating the image identification code according to the two-dimensional code array value specifically includes,
step S2041, correspondingly generating black and white image code points according to the two-dimensional code array value;
step S2042, combining the black and white image code points to obtain the image identification code according to the array arrangement structure of the two-dimensional code array values;
further, the step S3 of obtaining fault tolerance information about the encrypted security code in a predetermined space and generating an image identification resetting code about the image identification code according to the fault tolerance information specifically includes,
step S301, carrying out code point information identification processing on the encrypted security code about code point shape, code point size and code point distribution to obtain blank area distribution state information of the encrypted security code on a two-dimensional plane;
step S302, calculating to obtain fault tolerance rate information corresponding to the encrypted security code through a preset fault tolerance rate learning analysis model according to the distribution state information of the blank area;
step S303, according to the fault tolerance rate information, carrying out code point size scaling processing and/or code point position translation processing on the image identification code to obtain the image identification reset code;
further, in the step S4, performing a filling process on the space where the encrypted security code is located, so as to obtain the image security code specifically includes,
step S401, performing plane division processing on the space where the encrypted security code is located to obtain a plurality of plane sub-regions;
step S402, sequentially filling different code points corresponding to the image identification reset code into the accommodating space corresponding to the encryption security code according to a preset filling sequence.
Compared with the prior art, the image security code generation method based on the anti-counterfeiting encryption and the visual identification organically combines the encryption security code and the image identification code through special encryption processing and data coding processing to correspondingly obtain the image security code, and the image security code not only has the characteristics of high anti-counterfeiting security, large data capacity, high traceability and high fault tolerance of the encryption security code, but also has the characteristics of high identification degree and convenience for wide application of the image identification code, so that the intelligent identification performance and the application convenience of the image security code can be effectively improved, and the applicability of the intelligent identification code to different occasions is further improved.
Further, the method for generating an image security code based on anti-counterfeiting encryption and visual identification according to claim 2 is characterized in that:
the constructing a first feature matrix about the first object according to the first object extraction information includes:
transforming the information extracted from the first object into a first feature matrix A of order n x n:
wherein, b 1 ,b 2 ,…,b n Extracting an information indicator value in the information for said first object, a ij The values of i and j are 1,2,3, …, and n is the number of information indexes;
the first object extracts information to compare and determine a scale value q ij ：
Wherein, bi, b j Extracting an information indicator in the information for the first object,
then according to q ij Obtaining a scale matrix, determining the scale matrix as the first characteristic information, wherein the scale matrix Q = (Q) ij ) n×n ；
The performing encryption conversion processing on the first feature information includes:
and performing encryption conversion processing on the first characteristic information:
C＝P(A×Q) T P -1 -E
c is encrypted security feature information, P is a preset n multiplied by n order reversible matrix, Q is a scale matrix, and E is an n multiplied by n order identity matrix;
the performing on-transcoding processing on the encrypted security feature information to obtain the encrypted security code includes:
converting the encrypted security matrix into the encrypted security code:
Char＝mat2str(C)
wherein Char is the encrypted security code, and C is the encrypted security feature information.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an image security code generation method based on anti-counterfeiting encryption and visual identification provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an image security code generation method based on anti-counterfeit encryption and visual identification according to an embodiment of the present invention. The image security code generation method based on anti-counterfeiting encryption and visual identification comprises the following steps:
step S1, acquiring first characteristic information of a first object, and generating an encrypted security code according to the first characteristic information.
Preferably, in the step S1, acquiring first characteristic information on the first object, and generating the encrypted security code according to the first characteristic information specifically includes,
a step S101 of performing a first information extraction process on at least one of space, image, and audio on the first object to obtain first object extraction information;
step S102, according to the first object extraction information, a first feature matrix related to the first object is constructed, and matrix learning analysis processing is carried out on the first feature matrix to obtain first feature information;
step S103, carrying out encryption conversion processing on the first characteristic information to obtain encrypted security characteristic information;
and step S104, performing code conversion processing on the encrypted security feature information to obtain the encrypted security code.
Preferably, in this step S101, performing the first information extraction process on at least one of space, image, and audio on the first object to obtain the first object extraction information specifically includes,
step S1011, performing first information extraction processing on at least one of a two-dimensional space, a three-dimensional space, an image color, an image texture, an image contour, a sound intensity distribution, and a sound frequency distribution on the first object to obtain first primary-order extraction information;
in step S1012, error correction processing is performed on the first primary extraction information with respect to a data structure and/or data noise to obtain the first object extraction information.
Preferably, in the step S102, constructing a first feature matrix about the first object according to the first object extraction information, and performing matrix learning analysis processing on the first feature matrix to obtain the first feature information specifically includes,
step S1021, acquiring a plurality of characterization information of space dimension characteristics, image optical characteristics and sound and audio characteristics of the first object from the first object extraction information;
step S1022, performing information arrangement processing on the plurality of characterization information according to a preset feature arrangement condition to obtain the first feature matrix;
step S1023, performing matrix learning analysis processing on the first feature matrix through a preset security code neural network model to determine a matrix evolution parameter of the first feature matrix, thereby obtaining the first feature information.
Preferably, in step S103, performing encryption conversion processing on the first feature information to obtain encrypted security feature information specifically includes
A step S1031 of performing information arrangement processing on the first feature information about a two-dimensional space or a three-dimensional space to obtain two-dimensional lattice distribution structure information or three-dimensional lattice distribution structure information about the first feature information;
step S1032, according to a preset encryption mode, performs lattice structure arrangement transformation on the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information, so as to encrypt and convert the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information into the encrypted security feature information.
Preferably, in the step S104, performing the transcoding process on the encrypted security feature information to obtain the encrypted security code specifically includes,
step S1041, obtaining a data length, a data code repetition degree and a data code filling degree corresponding to the encryption security characteristic information, and optimizing a preset code conversion model according to the data length, the data code repetition degree and the data code filling degree;
step S1042, converting the encrypted security feature information into the encrypted security code according to the optimized predetermined code conversion model.
And S2, acquiring second characteristic information about the second object, and generating an image identification code according to the second characteristic information.
Preferably, in the step S2, acquiring second feature information on the second object, and generating the image recognition code according to the second feature information specifically includes,
step S201, acquiring a full-range image of the second object, wherein the full-range image is a background image including at least a part of information of the second object;
step S202, carrying out color gamut characteristic value extraction processing on the background image to generate a spatial color gamut distribution lattice related to the background image;
step S203, generating two-dimensional code array value information about the second object as the second feature information according to the spatial color gamut distribution lattice;
step S204, the image identification code is generated according to the two-dimensional code array value.
Preferably, in the step S201, a full-range image about the second object is acquired, wherein the full-range image is specifically included for a background image including at least a part of information of the second object,
step S2011 of performing a photographing process on the second object to obtain a primary-stage photographed image about the second object;
step S2012, judging whether the primary shot image has face information according to a preset face recognition algorithm;
and S2013, if the primary shot image does not have the face information, directly taking the primary shot image as a full-range image, and if the primary shot image has the face information, carrying out image corrosion processing on the primary shot image according to the face information to obtain a corresponding full-range image.
Preferably, in the step S202, performing color gamut feature value extraction processing on the background image to generate a spatial color gamut distribution lattice with respect to the background image specifically includes,
step S2021, performing color gamut feature extraction processing on the background image with respect to HSI to generate HSI component values with respect to hue H, saturation S, and lightness I of the background image;
step S2022, calculating a color gamut evaluation value corresponding to each pixel point in the background image according to the HIS component value;
step S2023, converting the color gamut information corresponding to each pixel point in the background image into the spatial color gamut distribution lattice on the two-dimensional plane according to the color gamut evaluation value corresponding to each pixel point.
Preferably, in the step S203, two-dimensional code array value information about the second object is generated according to the spatial color gamut distribution lattice to specifically include as the second characteristic information,
step S2031, performing digital conversion processing on each lattice of the spatial color gamut distribution lattice to obtain a corresponding lattice digital representation value;
step S2032, combining the dot matrix digital table pointers into the two-dimensional code array value information according to the arrangement state of all the dot matrices in the spatial color gamut distribution dot matrix.
Preferably, in the step S204, the generating the image identification code according to the two-dimensional code array value specifically includes,
step S2041, correspondingly generating black and white image code points according to the two-dimensional code array value;
step S2042, the black and white image code points are combined to obtain the image identification code according to the array arrangement structure of the two-dimensional code array values.
And S3, acquiring the fault-tolerant rate information of the encrypted security code in a preset space, and generating an image identification resetting code of the image identification code according to the fault-tolerant rate information.
Preferably, the step S3 of obtaining fault tolerance information on the encrypted security code in a predetermined space and generating an image recognition reset code on the image recognition code according to the fault tolerance information specifically includes,
step S301, carrying out code point information identification processing on the encrypted security code about the code point shape, the code point size and the code point distribution to obtain the blank area distribution state information of the encrypted security code on a two-dimensional plane;
step S302, calculating to obtain fault tolerance rate information corresponding to the encrypted security code through a preset fault tolerance rate learning analysis model according to the distribution state information of the blank area;
step S303, according to the fault tolerance information, performing a code point size scaling process and/or a code point position translation process on the image identification code to obtain the image identification reset code.
And S4, performing filling processing on the image identification reset code in the space where the encrypted security code is located to obtain the image security code.
Preferably, in the step S4, the filling process of the space where the encrypted security code is located with respect to the image identification reset code to obtain the image security code specifically includes,
step S401, performing plane division processing on the space where the encrypted security code is located to obtain a plurality of plane sub-regions;
step S402, sequentially filling different code points corresponding to the image identification reset code into the accommodating space corresponding to the encrypted security code according to a preset filling sequence.
It can be known from the content of the above embodiment that, in the image security code generation method based on the anti-counterfeiting encryption and the visual identification, the encryption security code and the image identification code are organically combined through the special encryption processing and the data encoding processing to correspondingly obtain the image security code, and the image security code not only has the characteristics of high anti-counterfeiting security, large data capacity, high traceability and high fault tolerance of the encryption security code, but also has the characteristics of high identification degree and wide application convenience of the image identification code, so that the intelligent identification performance and the application convenience of the image security code can be effectively improved, and the applicability of the intelligent identification code to different occasions is further improved.
Preferably, the method for generating an image security code based on anti-counterfeiting encryption and visual identification according to claim 2, is characterized in that:
the constructing a first feature matrix about the first object according to the first object extraction information includes:
transforming the information extracted from the first object into a first feature matrix A of order n × n:
wherein, b 1 ,b 2 ,…,b n Extracting an information indicator value in the information for said first object, a ij The values of i and j are 1,2,3, …, and n is the number of information indexes;
generally, the information index value refers to a spatial dimension, an image color number, a sound frequency, an image resolution, and the like.
The first object extraction information is compared to determine a scale value q ij ：
Wherein bi, bj is an information index in the first object extraction information,
generally, the spatial information indicator is at a first importance level, the image information indicator is at a second importance level, and the audio information indicator is at a third importance level.
Then according to q ij Obtaining a scale matrix, determining the scale matrix as the first characteristic information, wherein the scale matrix Q = (Q) ij ) n×n ；
The performing encryption conversion processing on the first feature information includes:
and performing encryption conversion processing on the first characteristic information:
C＝P(A×Q) T P -1 E
c is encrypted security feature information, P is a preset n multiplied by n order reversible matrix, Q is a scale matrix, and E is an n multiplied by n order identity matrix;
generally, P is any n × n order invertible matrix, taken here
The performing on-transcoding processing on the encrypted security feature information to obtain the encrypted security code includes:
converting the encrypted security matrix into the encrypted security code:
Char＝mat2str(C)
wherein Char is the encrypted security code, and C is the encrypted security feature information.
Has the advantages that: the encryption conversion processing can be carried out on the first object extraction information by utilizing the technology, so that the first characteristic information is encrypted, the more the number of information indexes in the first object extraction information is, the more accurate the description is carried out on the first object, and the higher the security performance of the encrypted security code obtained through encryption security processing is, so that a user can scan the encrypted security code through a mobile terminal to obtain the data information contained in the encrypted security code, thereby obtaining the first characteristic information of which the clear code cannot be obtained in an illegal way, further realizing the protection of the first characteristic information, and greatly improving the security of the information.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An image security code generation method based on anti-counterfeiting encryption and visual identification is characterized by comprising the following steps:
the method comprises the following steps of S1, acquiring first characteristic information about a first object, and generating an encrypted security code according to the first characteristic information;
s2, acquiring second characteristic information of a second object, and generating an image identification code according to the second characteristic information;
s3, acquiring fault tolerance rate information of the encrypted security code in a preset space, and generating an image identification reset code of the image identification code according to the fault tolerance rate information;
s4, filling the space where the encrypted security code is located with the image identification reset code to obtain the image security code;
in the step S1, acquiring first characteristic information about the first object, and generating the encrypted security code according to the first characteristic information specifically includes,
a step S101 of performing a first information extraction process on at least one of space, an image, and audio on the first object to obtain first object extraction information;
step S102, according to the first object extraction information, a first feature matrix about the first object is constructed, and matrix learning analysis processing is carried out on the first feature matrix to obtain first feature information;
step S103, carrying out encryption conversion processing on the first characteristic information to obtain encrypted security characteristic information;
step S104, carrying out code conversion processing on the encrypted security feature information to obtain the encrypted security code;
in the step S2, acquiring second feature information on a second object, and generating an image recognition code according to the second feature information specifically includes,
step S201, acquiring a full-range image of the second object, wherein the full-range image is a background image at least including part of information of the second object;
step S202, carrying out color gamut characteristic value extraction processing on the background image to generate a space color gamut distribution lattice related to the background image;
step S203, generating two-dimensional code array value information about the second object as the second feature information according to the spatial color gamut distribution lattice;
and step S204, generating the image identification code according to the two-dimensional code array value.
2. The method for generating image security codes based on anti-counterfeiting encryption and visual identification according to claim 1, characterized in that:
in the step S101, the performing of the first information extraction process on at least one of space, image, and audio on the first object to obtain the first object extraction information specifically includes,
step S1011 of performing first information extraction processing on at least one of a two-dimensional space, a three-dimensional space, an image color, an image texture, an image contour, a sound intensity distribution, and a sound frequency distribution on the first object to obtain first preliminary-order extraction information;
step S1012, performing error correction processing on the first primary-stage extraction information with respect to a data structure and/or data noise to obtain the first object extraction information;
alternatively, the first and second electrodes may be,
in the step S102, constructing a first feature matrix about the first object according to the first object extraction information, and performing matrix learning analysis processing on the first feature matrix to obtain the first feature information specifically includes,
step S1021, acquiring a plurality of characterization information of space dimension characteristics, image optical characteristics and sound audio characteristics of the first object from the first object extraction information;
step S1022, according to a preset feature arrangement condition, performing information arrangement processing on the plurality of characterization information to obtain the first feature matrix;
step S1023, performing matrix learning analysis processing on the first feature matrix through a preset security code neural network model to determine a matrix evolution parameter of the first feature matrix, thereby obtaining the first feature information.
3. The method for generating image security codes based on anti-counterfeiting encryption and visual identification as claimed in claim 1 or 2, wherein:
in step S103, performing encryption conversion processing on the first feature information to obtain encrypted security feature information specifically includes
A step S1031 of performing information arrangement processing on the first feature information about a two-dimensional space or a three-dimensional space to obtain two-dimensional lattice distribution structure information or three-dimensional lattice distribution structure information about the first feature information;
step S1032, according to a preset encryption mode, performing lattice structure arrangement transformation on the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information to encrypt and convert the two-dimensional lattice distribution structure information or the three-dimensional lattice distribution structure information into the encrypted security feature information;
alternatively, the first and second electrodes may be,
in the step S104, performing transcoding processing on the encrypted security feature information to obtain the encrypted security code specifically includes,
step S1041, obtaining a data length, a data code repetition degree and a data code filling degree corresponding to the encryption security characteristic information, and optimizing a preset code conversion model according to the data length, the data code repetition degree and the data code filling degree;
step S1042, converting the encrypted security feature information into the encrypted security code according to the optimized preset code conversion model.
4. The image security code generation method based on anti-counterfeiting encryption and visual identification as claimed in claim 1, characterized in that:
in step S201, a full-range image of the second object is acquired, wherein the full-range image is specifically included in a background image at least including a part of information of the second object,
step S2011 of performing shooting processing on the second object to obtain a primary shot image about the second object;
step S2012, judging whether the primary shot image has face information according to a preset face recognition algorithm;
and S2013, if the primary shot image does not have the face information, directly taking the primary shot image as a full-range image, and if the primary shot image has the face information, carrying out image corrosion processing on the primary shot image according to the face information to obtain a corresponding full-range image.
5. The method for generating image security codes based on anti-counterfeiting encryption and visual identification according to claim 1, characterized in that:
in the step S202, performing color gamut feature value extraction processing on the background image to generate a spatial color gamut distribution lattice with respect to the background image specifically includes,
step S2021, performing color gamut feature extraction processing on the background image with respect to HSI to generate HSI component values with respect to hue H, saturation S, and lightness I of the background image;
step S2022, calculating a color gamut evaluation value corresponding to each pixel point in the background image according to the HIS component value;
step S2023, converting the color gamut information corresponding to each pixel point in the background image into the spatial color gamut distribution lattice related to the two-dimensional plane according to the color gamut evaluation value corresponding to each pixel point.
6. The method for generating image security codes based on anti-counterfeiting encryption and visual identification according to claim 1, characterized in that:
in the step S203, two-dimensional code array value information on the second object is generated according to the spatial color gamut distribution lattice to specifically include as the second feature information,
step S2031, carrying out digital conversion treatment on each dot matrix of the spatial color gamut distribution dot matrix to obtain a corresponding dot matrix digital representation value;
step S2032, combining the dot matrix digital representation values into the two-dimensional code array value information according to the arrangement state of all the dot matrixes in the space color gamut distribution dot matrix;
alternatively, the first and second liquid crystal display panels may be,
in step S204, generating the image id code according to the two-dimensional code array value specifically includes,
step S2041, correspondingly generating black and white image code points according to the two-dimensional code array value;
step S2042, according to the array arrangement structure of the two-dimensional code array values, the black and white image code points are combined to obtain the image identification code.
7. The image security code generation method based on anti-counterfeiting encryption and visual identification as claimed in claim 1, characterized in that:
in the step S3, obtaining fault tolerance information about the encrypted security code in a predetermined space, and generating an image recognition reset code about the image recognition code according to the fault tolerance information specifically includes,
step S301, carrying out code point information identification processing on the encrypted security code about code point shape, code point size and code point distribution to obtain blank area distribution state information of the encrypted security code on a two-dimensional plane;
step S302, calculating to obtain fault tolerance rate information corresponding to the encrypted security code through a preset fault tolerance rate learning analysis model according to the distribution state information of the blank area;
step S303, according to the fault tolerance rate information, carrying out code point size scaling processing and/or code point position translation processing on the image identification code to obtain the image identification reset code.
8. The method for generating image security codes based on anti-counterfeiting encryption and visual identification according to claim 1, characterized in that:
in step S4, the filling process of the image identification reset code on the space where the encrypted security code is located to obtain the image security code specifically includes,
step S401, performing plane division processing on the space where the encrypted security code is located to obtain a plurality of plane sub-regions;
and S402, sequentially filling different code points corresponding to the image identification reset code into the accommodating space corresponding to the encryption security code according to a preset filling sequence.
9. The method for generating image security codes based on anti-counterfeiting encryption and visual identification according to claim 1, characterized in that:
the constructing a first feature matrix about the first object according to the first object extraction information includes:
transforming the first object extraction information into a first feature matrix A of order n' n:
wherein, b 1 ,b 2 ,...,b n Extracting an information indicator value in the information for said first object, a ij For the value of the ith row and the jth column, i and j are divided into 1,2,3 … n, and n is the number of information indexes;
the first object extraction information is compared to determine a scale value q ij ：
Wherein, b i ，b j Extracting an information indicator in the information for the first object,
then according to q ij Obtaining a scale matrix, determining the scale matrix as the first characteristic information, wherein the scale matrix Q = (Q) ij ) n×n ；
The performing encryption conversion processing on the first feature information includes: and performing encryption conversion processing on the first characteristic information:
C＝P(A×Q) T P -1 -E
c is encrypted security feature information, P is a preset n multiplied by n order reversible matrix, Q is a scale matrix, and E is an n multiplied by n order identity matrix;
the performing on-transcoding processing on the encrypted security feature information to obtain the encrypted security code includes:
converting the encrypted security matrix into the encrypted security code:
Char＝mat2str(C)
wherein Char is the encrypted security code, and C is the encrypted security feature information.
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