Electronic grating anti-counterfeiting identification method and system
By using the electronic grating anti-counterfeiting identification method, image processing and GAN neural networks are used to achieve automated anti-counterfeiting identification without physical grating sheets, which solves the problem of inconvenience in the use of existing grating anti-counterfeiting technologies and improves anti-counterfeiting efficiency and consumer trust.
Patent Information
- Application Number
- CN202511635265.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-10
- Publication Date
- 2026-02-06
AI Technical Summary
Existing grating anti-counterfeiting technology requires carrying a physical grating sheet, which is inconvenient to use, has low anti-counterfeiting efficiency, and makes it difficult for consumers to easily and accurately verify the authenticity of products.
The electronic grating anti-counterfeiting identification method is adopted. By correcting, enhancing and superimposing electronic grating images, image processing technology and GAN neural network are used to realize the automatic identification of anti-counterfeiting information. The anti-counterfeiting information can be displayed by scanning the image with a mobile phone.
It enables automated anti-counterfeiting identification without the need to carry physical grating sheets, improving anti-counterfeiting efficiency and accuracy, and enhancing consumer trust in products.
Smart Images
Figure CN121482583A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of anti-counterfeiting technology for product packaging, specifically an electronic grating anti-counterfeiting identification method and system. Background Technology
[0002] Currently, the market is flooded with counterfeit and substandard products. Consumers cannot easily and accurately verify anti-counterfeiting information, leading to a lack of consumer trust and consequently impacting brand reputation and sales. Regarding lenticular lens technology, the traditional application involves adding hidden information during the product printing process. To verify authenticity, a physical lenticular film is moved across the product surface to reveal the hidden image. However, current technology requires carrying a physical lenticular sheet for authentication, which is inconvenient and results in low anti-counterfeiting efficiency. Summary of the Invention
[0003] To address the shortcomings mentioned in the background art, the present invention aims to provide an electronic grating anti-counterfeiting identification method and system.
[0004] Firstly, the objective of this invention can be achieved through the following technical solution: an electronic grating anti-counterfeiting identification method, the method comprising the following steps: The system receives image data to be verified, performs image correction on the image data to be verified, corrects it to the same shape and size as the original image, and obtains a corrected image. The image correction is performed by extracting edge contour information and grayscale information from the image data to be verified. The corrected image is then enhanced to obtain an enhanced image. The image enhancement includes at least one of upsampling, adaptive histogram equalization, and GAN neural network. An electronic grating image is generated based on preset printing parameters. The electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified and output the anti-counterfeiting recognition result.
[0005] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the image data to be verified is obtained by collecting pre-made printed materials with anti-counterfeiting information.
[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the process of producing the printed material with anti-counterfeiting information, comprising: Receive carrier image data, convert the carrier image data from RGB color to CMYK color, and obtain image data of four channels: C, M, Y, and K. The error diffusion dithering algorithm is used to halftone process the image data of the four channels C, M, Y, and K to obtain the halftone image. One of the four channels C, M, Y, and K is selected as the target channel. The halftone dots of the preset anti-counterfeiting area in the halftone image corresponding to the target channel are shifted to obtain the image of the target channel after shifting. The image of the offset target channel is combined with the image data of the other three channels to obtain a printed product with anti-counterfeiting information.
[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: extracting the edge contour information and grayscale information respectively using an edge contour detection operator and a grayscale information calculation algorithm, wherein the edge contour detection operator is as follows: The formula for calculating grayscale information is as follows: In the formula, To match the response value; Template image information; For the image information to be matched; The width and height of the template image; This refers to the normalized template image information; This refers to the normalized image information to be matched.
[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: performing image enhancement on the corrected image, wherein the adaptive image enhancement calculation includes: In the formula, S k r represents the transformed grayscale value. k T(r) represents the k-th gray level in the original image. k ) is the grayscale mapping function, P r (r) i ) represents the grayscale level r i The probability, n i For grayscale value r i The number of pixels, where N is the total number of pixels in the image.
[0009] The loss function of the improved SRGAN neural network used for image enhancement is as follows: Content loss: in, This represents the feature mapping after the i-th pooling layer of the j-th convolutional layer in the pre-trained VGG network; , These are the width and height of the feature map, respectively; For true high-resolution images; Output an image to the generator; The losses incurred in combat are as follows: The total loss function is as follows: .
[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the calculation process for generating the electronic grating image based on preset printing parameters, comprising: Where d is the side length of the halftone unit, A is the dot area ratio of the hidden image, and B is the dot area ratio of the raster image.
[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: the calculation formula for offsetting the halftone dots of the preset anti-counterfeiting area in the halftone image corresponding to the target channel is as follows: Where x and y are the horizontal and vertical displacements of the hidden information, respectively, and P l P represents the horizontal resolution of the image. c α represents the vertical resolution of the image, and α is the halftone angle.
[0012] Secondly, in order to achieve the above objectives, the present invention discloses an electronic grating anti-counterfeiting identification system, comprising: An image processing module is used to receive image data to be verified, perform image correction on the image data to be verified, correct it to the same shape and size as the original image, and obtain a corrected image. The image correction is performed by extracting edge contour information and grayscale information from the image data to be verified. The corrected image is then enhanced to obtain an enhanced image. The image enhancement includes at least one of upsampling, adaptive histogram equalization, and GAN neural network. The anti-counterfeiting identification module is used to generate an electronic grating image based on preset printing parameters. The electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified and output the anti-counterfeiting identification result.
[0013] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs an electronic grating anti-counterfeiting identification method as described above.
[0014] In another aspect of the present invention, in order to achieve the above-mentioned objective, a computer-readable storage medium is disclosed, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is loaded and executed by a processor, an electronic grating anti-counterfeiting identification method as described above is employed.
[0015] The beneficial effects of this invention are: This invention uses an electronic grating instead of a film grating sheet for hidden information detection. Through an automated image correction scheme, it eliminates optical and shooting error interference. It adopts an internal image enhancement algorithm to achieve micron-level recognition of dots. Furthermore, anti-counterfeiting information can be displayed and authenticity can be verified simply by scanning the image with a mobile phone. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the workflow of the present invention; Figure 3 This is a schematic diagram of the system structure of the present invention; Figure 4 This is a printed image of the present invention with anti-counterfeiting information; Figure 5 This is a printed image of the present invention with anti-counterfeiting information; Figure 6 This is the electron grating pattern of the present invention; Figure 7 This is the anti-counterfeiting information letter CQS image identified by this invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Example 1: like Figure 1 As shown, an electronic grating anti-counterfeiting identification method includes the following steps: S101: Receive the image data to be verified, perform image correction on the image data to be verified, correct it to the same shape and size as the original image, and obtain the corrected image, wherein the image correction is performed by extracting edge contour information and grayscale information from the image data to be verified; perform image enhancement on the corrected image to obtain the enhanced image, wherein the image enhancement includes at least one of upsampling, adaptive histogram equalization and GAN neural network. The image data to be verified was obtained by collecting pre-made printed materials with anti-counterfeiting information.
[0019] The production process of printed materials with anti-counterfeiting information includes: Receive carrier image data, convert the carrier image data from RGB color to CMYK color, and obtain image data of four channels: C, M, Y, and K. The Floyd-Steinberg algorithm is used to halftone process the image data of the C, M, Y, and K channels to obtain the halftone image. The target channel of the C, M, Y, and K channels is selected, and the halftone dots of the preset anti-counterfeiting area in the halftone image of the target channel are offset to obtain the offset target channel image. The image of the offset target channel is combined with the image data of the other three channels to obtain a printed product with anti-counterfeiting information.
[0020] The formula for offsetting the halftone dots of the preset anti-counterfeiting area in the halftone image corresponding to the target channel is as follows: Where x and y are the horizontal and vertical displacements of the hidden information, respectively, and P l P represents the horizontal resolution of the image. c α represents the vertical resolution of the image, and α is the halftone angle.
[0021] Specifically, select version C, and shift the halftone dots shaped like the letters CQS to obtain a new cyan version C'; Edge contour information and grayscale information are extracted using an edge contour detection operator and a grayscale information calculation algorithm, respectively. The edge contour detection operator is as follows: The formula for calculating the correlation of grayscale template matching is as follows: In the formula, To match the response value; Template image information; For the image information to be matched; The width and height of the template image; This refers to the normalized template image information; This refers to the normalized image information to be matched.
[0022] To match the response value; Template image information; For the image information to be matched; The width and height of the template image; This refers to the normalized template image information; This refers to the normalized image information to be matched. The corrected image is then enhanced, with adaptive image enhancement calculated as follows: including: In the formula, S k r represents the transformed grayscale value. k T(r) represents the k-th gray level in the original image. k ) is the grayscale mapping function, P r (r) i ) represents the grayscale level r i The probability, n i For grayscale value r i The number of pixels, where N is the total number of pixels in the image.
[0023] The loss function of the improved SRGAN neural network used for image enhancement is as follows: Content loss: in, This represents the feature mapping after the i-th pooling layer of the j-th convolutional layer in the pre-trained VGG network; , These are the width and height of the feature map, respectively; For true high-resolution images; Output an image to the generator; The losses incurred in combat are as follows: The total loss function is as follows: .
[0024] S102: Generate an electronic grating image based on preset printing parameters, wherein the electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified, and output the anti-counterfeiting recognition result.
[0025] The calculation process for generating an electronic grating image based on preset printing parameters includes: Where d is the side length of the halftone unit, A is the dot area ratio of the hidden image, and B is the dot area ratio of the raster image.
[0026] Example 2: To achieve the above objective, such as Figure 3 As shown, based on Embodiment 1, this invention discloses an electronic grating anti-counterfeiting identification system, comprising: Image processing module 11 is used to receive image data to be verified, perform image correction on the image data to be verified, correct it to the same shape and size as the original image, and obtain a corrected image. The image correction is performed by extracting edge contour information and grayscale information from the image data to be verified. The corrected image is then enhanced to obtain an enhanced image. The image enhancement includes at least one of upsampling, adaptive histogram equalization, and GAN neural network. The anti-counterfeiting identification module 12 is used to generate an electronic grating image based on preset printing parameters. The electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified and output the anti-counterfeiting identification result.
[0027] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.
[0028] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0029] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0030] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.
Claims
1. A method for anti-counterfeiting identification using electronic gratings, characterized in that, The method includes the following steps: The system receives image data to be verified, performs image correction on the image data to be verified, corrects it to the same shape and size as the original image, and obtains a corrected image. The image correction is performed by extracting edge contour information and grayscale information from the image data to be verified. The corrected image is then enhanced to obtain an enhanced image. The image enhancement includes at least one of upsampling, adaptive histogram equalization, and GAN neural network. An electronic grating image is generated based on preset printing parameters. The electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified and output the anti-counterfeiting recognition result.
2. The electronic grating anti-counterfeiting identification method according to claim 1, characterized in that, The image data to be verified is obtained by collecting pre-made printed materials with anti-counterfeiting information.
3. The electronic grating anti-counterfeiting identification method according to claim 2, characterized in that, The production process of the printed materials with anti-counterfeiting information includes: Receive carrier image data, convert the carrier image data from RGB color to CMYK color, and obtain image data of four channels: C, M, Y, and K. The error diffusion dithering algorithm is used to halftone process the image data of the four channels C, M, Y, and K to obtain the halftone image. One of the four channels C, M, Y, and K is selected as the target channel. The halftone dots of the preset anti-counterfeiting area in the halftone image corresponding to the target channel are shifted to obtain the image of the target channel after shifting. The image of the offset target channel is fused with the image data of the other three channels, and then printed using a printing press to obtain printed materials with anti-counterfeiting information.
4. The electronic grating anti-counterfeiting identification method according to claim 1, characterized in that, The edge contour information and grayscale information are extracted using an edge contour detection operator and a grayscale information calculation algorithm, respectively. The edge contour detection operator is as follows: The formula for calculating the similarity of grayscale information is as follows: In the formula, To match the response value; Template image information; For the image information to be matched; The width and height of the template image; This refers to the normalized template image information; This refers to the normalized image information to be matched.
5. The electronic grating anti-counterfeiting identification method according to claim 1, characterized in that, The calculation process for enhancing the corrected image includes: In the formula, S k r represents the transformed grayscale value. k T(r) represents the k-th gray level in the original image. k ) is the grayscale mapping function, P r (r) i ) represents the grayscale level r i The probability, n i For grayscale value r i The number of pixels, where N is the total number of pixels in the image; The loss function of the improved SRGAN neural network for image enhancement is as follows: Content loss: in, This represents the feature mapping after the i-th pooling layer of the j-th convolutional layer in the pre-trained VGG network; , These are the width and height of the feature map, respectively; For true high-resolution images; Output an image to the generator; The losses incurred in combat are as follows: The total loss function is as follows: 。 6. The electronic grating anti-counterfeiting identification method according to claim 1, characterized in that, The calculation process for generating an electronic grating image based on preset printing parameters includes: Where d is the side length of the halftone unit, A is the dot area ratio of the hidden image, and B is the dot area ratio of the raster image.
7. The electronic grating anti-counterfeiting identification method according to claim 3, characterized in that, The calculation formula for offsetting the halftone dots of the preset anti-counterfeiting area in the halftone image corresponding to the target channel is as follows: Where x and y are the horizontal and vertical displacements of the hidden information, respectively, and P l P represents the horizontal resolution of the image. c α represents the vertical resolution of the image, and α is the halftone angle.
8. An electronic grating anti-counterfeiting identification system, employing the electronic grating anti-counterfeiting identification method according to any one of claims 1 to 7, characterized in that, include: An image processing module is used to receive image data to be verified, perform image correction on the image data to be verified, correct it to the same shape and size as the original image, and obtain a corrected image. The image correction is performed by extracting edge contour information and grayscale information from the image data to be verified. The corrected image is then enhanced to obtain an enhanced image. The image enhancement includes at least one of upsampling, adaptive histogram equalization, and GAN neural network. The anti-counterfeiting identification module is used to generate an electronic grating image based on preset printing parameters. The electronic grating image is a black and white striped image. The electronic grating image is superimposed on the enhanced image. The black areas of the electronic grating image are opaque, while the white areas are translucent, so as to display the anti-counterfeiting information in the image data to be verified and output the anti-counterfeiting identification result.
9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs an electronic grating anti-counterfeiting identification method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it employs an electronic grating anti-counterfeiting identification method according to any one of claims 1 to 7.