Random distribution-based feature article anti-counterfeit label encryption system

Abstract

The invention discloses an article anti-counterfeit label encryption system based on random distribution characteristics, and relates to the technical field of product anti-counterfeit. Comprising an anti-counterfeit label containing anti-counterfeit authentication information, a feature recognition database storing the anti-counterfeit authentication information, an intelligent reading instrument obtaining the anti-counterfeit authentication information and an anti-counterfeit verification server responding to an authentication request. The anti-counterfeiting authentication information is generated by encrypting a data set constructed by combining the feature information of the feature information object and the product information of the product to be subjected to anti-counterfeiting, and is recorded on the anti-counterfeiting label in the form of a two-dimensional code, a bar code or a digital sequence; the feature information is formed by injecting or transferring a feature information object into the anti-counterfeiting area in a random distribution manner by using a Fourier function, and the anti-counterfeiting authentication server decrypts the uploaded anti-counterfeiting authentication information to restore the feature information and the product information of the product to be subjected to anti-counterfeiting. And comparing the anti-counterfeiting authentication information with the anti-counterfeiting authentication information stored in the feature recognition database to generate a verification result.

Description

Technical Field

[0001] The present invention relates to the technical field of product anti-counterfeiting, and in particular to an encryption system for anti-counterfeiting labels of characteristic items based on random distribution. Background Art

[0002] In today's globalized market, the problem of counterfeit and substandard products is growing increasingly serious, causing significant losses to consumers and businesses alike. Counterfeit products not only harm the legitimate rights and interests of consumers but also negatively impact a company's brand image and market reputation. Therefore, developing efficient and reliable anti-counterfeiting technologies has become a crucial issue for protecting intellectual property rights and maintaining market order.

[0003] Traditional anti-counterfeiting technologies primarily include physical, chemical, and digital technologies. While physical technologies like laser and watermarks can provide some degree of protection, they are susceptible to duplication and counterfeiting due to advancements in counterfeiting technology. Chemical technologies like fluorescent and temperature-dependent technologies, while unique, also face the risk of being cracked. Digital technologies like QR codes and SMS verification, while convenient and fast, carry the potential for data tampering and forgery.

[0004] In recent years, with the rapid development of information technology, encryption technology has been widely used in the field of anti-counterfeiting. However, existing encryption anti-counterfeiting technologies mostly rely on fixed encryption algorithms and keys. Once these algorithms and keys are cracked, the anti-counterfeiting system will be ineffective. In addition, existing anti-counterfeiting technologies often fail to utilize the inherent characteristics of the product, making anti-counterfeiting labels easy to forge. Therefore, a new anti-counterfeiting technology is needed that can combine the random characteristics of the product itself with advanced encryption technology to generate unique and non-replicable anti-counterfeiting labels, thereby effectively preventing the circulation of counterfeit and shoddy products. To this end, this paper proposes an encryption system for anti-counterfeiting labels based on randomly distributed features. Summary of the Invention

[0005] The main purpose of the present invention is to provide an encryption system for anti-counterfeiting labels based on randomly distributed characteristic information objects on the anti-counterfeiting labels and combine them with encryption technology to generate unique anti-counterfeiting authentication information, providing a new solution for the anti-counterfeiting field and effectively solving the problems in the background technology.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0007] The anti-counterfeiting label encryption system for items based on random distribution of features includes:

[0008] An anti-counterfeiting label containing the unique anti-counterfeiting authentication information of the product to be protected;

[0009] The anti-counterfeiting authentication information is recorded on the anti-counterfeiting label in the form of a QR code, a barcode, or a digital sequence. The anti-counterfeiting authentication information is generated by encrypting a data set using an encryption algorithm. The data set is constructed by combining characteristic information of a characteristic information object with product information of the product to be anti-counterfeited. The product to be anti-counterfeited is provided with an anti-counterfeiting area. The characteristic information is formed by injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function.

[0010] A feature recognition database for storing the anti-counterfeiting authentication information;

[0011] The feature recognition database is also used to store image information of the anti-counterfeiting area of ​​the anti-counterfeiting product;

[0012] An intelligent reading instrument for obtaining the anti-counterfeiting authentication information on the anti-counterfeiting label;

[0013] The intelligent reading instrument generates an authentication request while obtaining the anti-counterfeiting authentication information;

[0014] an anti-counterfeiting verification server for responding to the authentication request;

[0015] After receiving the authentication request from the intelligent reading instrument, the anti-counterfeiting verification server decrypts the anti-counterfeiting authentication information uploaded by the intelligent reading instrument to restore the feature information and product information of the product to be anti-counterfeited, compares the restored feature information and product information with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency, and generates a verification result and feeds it back to the intelligent reading instrument.

[0016] Furthermore, the generation process of the anti-counterfeiting authentication information includes the following steps:

[0017] Extracting the randomly distributed characteristic information from the anti-counterfeiting label;

[0018] Combining the product information of the product to be prevented from counterfeiting with the extracted feature information to form a complete data set for the product to be prevented from counterfeiting;

[0019] removing noise and outliers from the data set and converting the feature information and the product information into a unified format;

[0020] Generate a key using a key generation algorithm, encrypt the integrated data using an AES algorithm to obtain encrypted data, encrypt the key using an RSA algorithm to obtain an encryption key, and generate a hash value for the encrypted data using a SHA-256 algorithm;

[0021] combining the encrypted data, the encryption key, and the hash value to form the anti-counterfeiting authentication information;

[0022] The anti-counterfeiting authentication information is encoded in the form of a QR code, a bar code or a digital sequence.

[0023] Furthermore, the specific process of forming the characteristic information by injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function includes the following steps:

[0024] Define the Fourier descriptor of the characteristic information object. Specifically, assume that the polar coordinates of the characteristic information object injected or transferred into the anti-counterfeiting area in a randomly distributed manner are represented by (r, θ), where r is the distance from the center to the characteristic information object and θ is the angle. Then, the distribution of the characteristic information object is represented by the Fourier descriptor as follows:

[0025]

[0026] Among them, D n is the amplitude of the n-th order Fourier descriptor; δ n is the phase angle of the nth-order Fourier descriptor; N is the highest order of the Fourier descriptor;

[0027] Randomly generate the phase angle δ n , specifically for each n, the phase angle δ n Uniformly distributed in the interval [-π,π];

[0028] By choosing different Fourier descriptors D n controlling the characteristics of the characteristic information object;

[0029] According to any given set of random phase angles δ n and the Fourier descriptor D n , the position r(θ) of each characteristic information object is calculated by the formula:

[0030] r(θ)=D0+D2cos(2θ+δ2)+D3cos(3θ+δ3)+...+D N cos(Nθ+δ N );

[0031] By connecting all the calculated positions of the characteristic information objects, the characteristic distribution of the characteristic information objects can be mapped out to form the characteristic information.

[0032] Furthermore, the product information includes one or more combinations of product number, production date, batch number or manufacturer information.

[0033] Furthermore, the features include base radius, aspect ratio, profile irregularity and surface roughness; the Fourier descriptor D that controls the features n The definition is as follows:

[0034] Selecting Fourier descriptor D0 to control the base radius;

[0035] Selecting Fourier descriptor D2 to control the aspect ratio;

[0036] Selecting Fourier descriptor D3 to control the irregularity of the feature profile;

[0037] Fourier descriptor D8 is selected to control the surface roughness.

[0038] Furthermore, the implementation process of the system includes the following steps:

[0039] Step a: Anti-counterfeiting label production

[0040] The specific steps include:

[0041] Step s11, using the product to be protected from counterfeiting as a carrier, and dividing the carrier into anti-counterfeiting areas;

[0042] Step s12, injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner to form a unique random feature;

[0043] Step s13, capturing images of the anti-counterfeiting area, recording the distribution of characteristic information objects, and storing them in a computer's feature recognition database;

[0044] Step b: encryption

[0045] Step s21, extracting characteristic information on the anti-counterfeiting label;

[0046] Step s22, combining the extracted feature information with the product information to form a complete data set;

[0047] Step s23, encrypting the data set using an encryption algorithm to generate unique anti-counterfeiting authentication information;

[0048] Step s24, recording the encrypted anti-counterfeiting authentication information on the anti-counterfeiting label in the form of a QR code, barcode or digital sequence;

[0049] Step c, verification process

[0050] Step s31: The user terminal scans the QR code, barcode or digital sequence recorded on the anti-counterfeiting label through an intelligent reading instrument to obtain the encrypted anti-counterfeiting authentication information;

[0051] Step s32: the anti-counterfeiting verification server decrypts the encrypted anti-counterfeiting authentication information to restore the feature information and product information;

[0052] Step s33, comparing the restored feature information and product information with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency. If they are consistent, it is judged to be authentic; otherwise, it is judged to be a counterfeit;

[0053] Step s34: generating a verification result and feeding it back to the intelligent reading instrument at the user end.

[0054] The present invention has the following beneficial effects:

[0055] Compared with the existing technology, this solution constructs an anti-counterfeiting label encryption system that contains unique anti-counterfeiting authentication information of the product to be anti-counterfeited, a feature recognition database for storing the anti-counterfeiting authentication information, an intelligent reading instrument for obtaining the anti-counterfeiting authentication information on the anti-counterfeiting label, and an anti-counterfeiting verification server for responding to authentication requests. The anti-counterfeiting authentication information is recorded on the anti-counterfeiting label in the form of a QR code, a barcode or a digital sequence. The anti-counterfeiting authentication information is generated by encrypting a data set using an encryption algorithm. The data set is constructed by combining the feature information of the feature information object with the product information of the product to be anti-counterfeited. The product to be anti-counterfeited is provided with an anti-counterfeiting area. The feature information is formed by injecting or transferring the feature information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function. The anti-counterfeiting verification server decrypts the anti-counterfeiting authentication information uploaded by the intelligent reading instrument, restores the feature information and product information of the product to be anti-counterfeited, and compares them with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency, and generates a verification result and feeds it back to the intelligent reading instrument. The following technical effects can be achieved:

[0056] 1) The random features on each anti-counterfeiting label are unique, which is equivalent to giving each product a unique anti-counterfeiting ID card, effectively preventing the circulation of counterfeit and inferior products;

[0057] 2) Using encryption technology to process feature information, the generated anti-counterfeiting authentication information is difficult to crack and tamper with, ensuring the security and reliability of the anti-counterfeiting information;

[0058] 3) The user end can quickly and accurately identify the anti-counterfeiting label through special instruments or smart phones and other tools. The operation is simple and improves the efficiency of anti-counterfeiting verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a schematic diagram of the overall structure of the encryption system for anti-counterfeiting labels of items based on random distribution of features according to the present invention;

[0060] Figure 2 Schematic diagram of the generation process of anti-counterfeiting authentication information of the present invention;

[0061] Figure 3 A schematic diagram of the characteristic information formation process of the present invention;

[0062] Figure 4 The figure is a schematic diagram of the implementation process of the anti-counterfeiting label encryption system of the present invention based on randomly distributed feature items. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with specific embodiments. The accompanying drawings are for illustrative purposes only and represent only schematic diagrams rather than actual drawings. They should not be understood as limiting the present invention. In order to better illustrate the specific embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0064] The specific implementation process of the technical solution of the present invention includes the following steps:

[0065] Step 1: Make an anti-counterfeiting label containing the unique anti-counterfeiting authentication information of the product to be protected;

[0066] The anti-counterfeiting authentication information is recorded on the anti-counterfeiting label in the form of a QR code, barcode or digital sequence. The anti-counterfeiting authentication information is generated by encrypting a data set using an encryption algorithm. The data set is constructed by combining the characteristic information of the characteristic information object with the product information of the product to be anti-counterfeited. The product to be anti-counterfeited is provided with an anti-counterfeiting area. The characteristic information is formed by injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function.

[0067] Specifically, the process of generating anti-counterfeiting authentication information includes the following steps:

[0068] s11: Integration of feature information and product information

[0069] s111: Feature information extraction: Extract randomly distributed feature information from anti-counterfeiting labels, such as the position, size, shape, etc. of fibers and particles.

[0070] s112: Product information integration: Merge product information (such as product number, production date, batch number, manufacturer information, etc.) with feature information to form a complete data set for the product to be anti-counterfeited.

[0071] s12: Data preprocessing

[0072] s121: Data cleaning: Remove noise and outliers from data to ensure data accuracy and consistency.

[0073] s122: Data formatting: Convert feature information and product information into a unified format for subsequent processing.

[0074] s13: Encryption algorithm selection

[0075] Symmetric encryption algorithms: such as AES (Advanced Encryption Standard), are suitable for quickly encrypting large amounts of data.

[0076] Asymmetric encryption algorithms: such as RSA, are suitable for encrypting small amounts of data, such as keys.

[0077] Hash algorithm: such as SHA-256, used to generate a unique summary of data and ensure data integrity.

[0078] s14: Encryption process

[0079] s141: Generate Key: Generate symmetric encryption keys and asymmetric encryption key pairs using a secure key generation algorithm.

[0080] s142: Symmetric encryption: Use the AES algorithm to encrypt the integrated data to obtain encrypted data.

[0081] s143: Asymmetric encryption: Use the RSA algorithm to encrypt the AES key to ensure secure key transmission.

[0082] s144: Hash generation: Use the SHA-256 algorithm to generate a hash value for the encrypted data as a unique identifier of the data.

[0083] s15: Anti-counterfeiting authentication information generation

[0084] s151: Combined encrypted data: The encrypted data, the encrypted AES key and the hash value are combined to form anti-counterfeiting authentication information.

[0085] s152: Coding formatting: Encode anti-counterfeiting authentication information into a QR code, barcode or digital sequence for easy storage and scanning.

[0086] s16: Storage and Verification

[0087] Storage: Storing anti-counterfeiting authentication information on an anti-counterfeiting label, such as a QR code or a digital sequence.

[0088] Verification: During verification, the anti-counterfeiting label is scanned, the data is decoded and decrypted, and the consistency of the hash value is verified to ensure that the data has not been tampered with.

[0089] Encryption technology is used to process characteristic information, and the generated anti-counterfeiting authentication information is difficult to crack and tamper with, ensuring the security and reliability of the anti-counterfeiting information.

[0090] The specific process of forming characteristic information by injecting or transferring characteristic information objects into the anti-counterfeiting area in a random distribution manner using Fourier function includes the following steps:

[0091] s31: Define the Fourier descriptor of the characteristic information object.

[0092] In one possible embodiment, the characteristic information object may be color;

[0093] Specifically, the polar coordinates of the characteristic information object (i.e., color) injected or transferred into the anti-counterfeiting area in a randomly distributed manner are assumed to be (r, θ), where r is the distance from the center to the color feature and θ is the angle. The distribution of the color feature is represented by the Fourier descriptor as follows:

[0094]

[0095] Among them, D n is the amplitude of the n-th order Fourier descriptor; δ n is the phase angle of the nth-order Fourier descriptor; N is the highest order of the Fourier descriptor;

[0096] s32: Randomly generate phase angle δ n , specifically for each n, the phase angle δ n Evenly distributed in the interval [-π,π], so even if D n Remain unchanged, different frontal phase angle δ n It also leads to different distribution of color features;

[0097] s33: By choosing different Fourier descriptors D n Control the shape and size of color features. Specifically, color features include base radius (i.e., the average size of color features), aspect ratio, contour irregularity, and surface roughness; control the Fourier descriptor D of the features. n The definition is as follows:

[0098] Select the Fourier descriptor D0 to control the base radius of the color feature;

[0099] Select Fourier descriptor D2 to control the aspect ratio of color features;

[0100] The Fourier descriptor D3 is selected to control the irregularity of the feature contour of the color feature;

[0101] Select Fourier descriptor D8 to control the surface roughness of color features;

[0102] It is important to note that the subscripts chosen for the Fourier descriptors have specific meaning when generating random features, and these choices affect the shape and distribution of the features. Each subscript n corresponds to a different frequency component, thus affecting the specific properties of the feature. The following is an explanation of these subscript choices:

[0103] Base radius

[0104] Subscript 0: Represents the base radius, or the average size of a feature. This is the basis term in all Fourier descriptors, providing a starting size and shape for the feature. Without this basis term, the size and shape of a feature would be completely determined by other terms, potentially resulting in overly complex or irregular features.

[0105] Aspect ratio

[0106] Subscript 2: Affects the aspect ratio of the feature. This term introduces a second-order cosine function, making the feature longer or wider in some directions than in others. This can simulate many shapes found in nature, such as ellipses or elongated shapes.

[0107] Irregularity of feature contours

[0108] Subscript 3: Introduces a third-order cosine function to increase the irregularity of the feature outline. This term makes the edge of the feature more complex and irregular, similar to many shapes in nature, such as the outline of leaves or rocks.

[0109] surface roughness

[0110] Subscript 8: An eighth-order cosine function is introduced to control the surface roughness of the feature. This term makes the surface of the feature more rough and complex, simulating the texture of sandpaper or some natural surfaces.

[0111] s34: According to any given set of random phase angles δ n and the Fourier descriptor D n , the position r(θ) of each characteristic information object is calculated by the formula:

[0112] r(θ)=D0+D2cos(2θ+δ2)+D3cos(3θ+δ3)+...+D N cos(Nθ+δ N );

[0113] s35: By connecting the positions of all calculated characteristic information objects, the characteristic distribution of the characteristic information objects can be mapped out to form characteristic information.

[0114] Through the above steps, the random features on each anti-counterfeiting label are unique, which is equivalent to giving each product an exclusive anti-counterfeiting ID card, effectively preventing the circulation of counterfeit and shoddy products.

[0115] Step 2: Verification Process

[0116] s21: The user scans the QR code, barcode or digital sequence on the anti-counterfeiting label through an intelligent reading device on the user side (such as a smart phone) to obtain the encrypted anti-counterfeiting authentication information, and at the same time generates an authentication request and sends it to the anti-counterfeiting verification server.

[0117] s22: After receiving the authentication request from the intelligent reading instrument, the anti-counterfeiting verification server decrypts the anti-counterfeiting authentication information uploaded by the intelligent reading instrument to restore the feature information and product information of the product to be anti-counterfeited.

[0118] s23: Compare the restored feature information and product information with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency, and generate a verification result to feed back to the intelligent reading instrument.

[0119] The user end can use special instruments or smart phones and other tools to quickly and accurately identify anti-counterfeiting labels. The operation is simple and improves the efficiency of anti-counterfeiting verification.

[0120] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. The anti-counterfeiting label encryption system based on random distribution features is characterized by: include: An anti-counterfeiting label containing the unique anti-counterfeiting authentication information of the product to be protected; The anti-counterfeiting authentication information is recorded on the anti-counterfeiting label in the form of a QR code, a barcode, or a digital sequence. The anti-counterfeiting authentication information is generated by encrypting a data set using an encryption algorithm. The data set is constructed by combining characteristic information of a characteristic information object with product information of the product to be anti-counterfeited. The product to be anti-counterfeited is provided with an anti-counterfeiting area. The characteristic information is formed by injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function. A feature recognition database for storing the anti-counterfeiting authentication information; The feature recognition database is also used to store image information of the anti-counterfeiting area of ​​the anti-counterfeiting product; An intelligent reading instrument for obtaining the anti-counterfeiting authentication information on the anti-counterfeiting label; The intelligent reading instrument generates an authentication request while obtaining the anti-counterfeiting authentication information; an anti-counterfeiting verification server for responding to the authentication request; After receiving the authentication request from the intelligent reading instrument, the anti-counterfeiting verification server decrypts the anti-counterfeiting authentication information uploaded by the intelligent reading instrument to restore the feature information and product information of the product to be anti-counterfeited, compares the restored feature information and product information with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency, and generates a verification result and feeds it back to the intelligent reading instrument.

2. The random distribution-based anti-counterfeiting label encryption system according to claim 1 is characterized in that: The generation process of the anti-counterfeiting authentication information includes the following steps: Extracting the randomly distributed characteristic information from the anti-counterfeiting label; Combining the product information of the product to be prevented from counterfeiting with the extracted feature information to form a complete data set for the product to be prevented from counterfeiting; removing noise and outliers from the data set and converting the feature information and the product information into a unified format; Generate a key using a key generation algorithm, encrypt the integrated data using an AES algorithm to obtain encrypted data, encrypt the key using an RSA algorithm to obtain an encryption key, and generate a hash value for the encrypted data using a SHA-256 algorithm; combining the encrypted data, the encryption key, and the hash value to form the anti-counterfeiting authentication information; The anti-counterfeiting authentication information is encoded in the form of a QR code, a bar code or a digital sequence.

3. The random distribution-based anti-counterfeiting label encryption system according to claim 1, characterized in that: The product information includes one or more combinations of product number, production date, batch number or manufacturer information.

4. The random distribution-based anti-counterfeiting label encryption system according to claim 1, characterized in that: The specific process of forming the characteristic information by injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner using a Fourier function includes the following steps: Define the Fourier descriptor of the characteristic information object. Specifically, assume that the polar coordinates of the characteristic information object injected or transferred into the anti-counterfeiting area in a randomly distributed manner are represented by (r, θ), where r is the distance from the center to the characteristic information object and θ is the angle. Then, the distribution of the characteristic information object is represented by the Fourier descriptor as follows: Among them, D n is the amplitude of the n-th order Fourier descriptor; δ n is the phase angle of the nth-order Fourier descriptor; N is the highest order of the Fourier descriptor; Randomly generate the phase angle δ n , specifically for each n, the phase angle δ n Uniformly distributed in the interval [-π,π]; By choosing different Fourier descriptors D n controlling the characteristics of the characteristic information object; According to any given set of random phase angles δ n and the Fourier descriptor D n , the position r(θ) of each characteristic information object is calculated by the formula: r(θ)=D0+D2cos(2θ+δ2)+D3cos(3θ+δ3)+...+D N cos(Nθ+δ N ); By connecting all the calculated positions of the characteristic information objects, the characteristic distribution of the characteristic information objects can be mapped out to form the characteristic information.

5. The random distribution-based feature anti-counterfeiting label encryption system according to claim 4 is characterized in that: The features include base radius, aspect ratio, profile irregularity and surface roughness; the Fourier descriptor D that controls the features n The definition is as follows: Selecting Fourier descriptor D0 to control the base radius; Selecting Fourier descriptor D2 to control the aspect ratio; Selecting Fourier descriptor D3 to control the irregularity of the feature profile; Fourier descriptor D8 is selected to control the surface roughness.

6. The random distribution-based feature anti-counterfeiting label encryption system according to any one of claims 1 to 5, characterized in that: The implementation process of the system includes the following steps: Step a: Anti-counterfeiting label production The specific steps include: Step s11, using the product to be protected from counterfeiting as a carrier, and dividing the carrier into anti-counterfeiting areas; Step s12, injecting or transferring the characteristic information object into the anti-counterfeiting area in a randomly distributed manner to form a unique random feature; Step s13, capturing images of the anti-counterfeiting area, recording the distribution of characteristic information objects, and storing them in a computer's feature recognition database; Step b: encryption Step s21, extracting characteristic information on the anti-counterfeiting label; Step s22, combining the extracted feature information with the product information to form a complete data set; Step s23, encrypting the data set using an encryption algorithm to generate unique anti-counterfeiting authentication information; Step s24, recording the encrypted anti-counterfeiting authentication information on the anti-counterfeiting label in the form of a QR code, barcode or digital sequence; Step c, verification process Step s31: The user terminal scans the QR code, barcode or digital sequence recorded on the anti-counterfeiting label through an intelligent reading instrument to obtain the encrypted anti-counterfeiting authentication information; Step s32: the anti-counterfeiting verification server decrypts the encrypted anti-counterfeiting authentication information to restore the feature information and product information; Step s33, comparing the restored feature information and product information with the anti-counterfeiting authentication information stored in the feature recognition database to verify their consistency. If they are consistent, it is judged to be authentic; otherwise, it is judged to be a counterfeit; Step s34: generating a verification result and feeding it back to the intelligent reading instrument at the user end.

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