WO2021022501A1 - 具有随机微点特征的防伪产品及其制作方法和验证方法 - Google Patents

具有随机微点特征的防伪产品及其制作方法和验证方法 Download PDF

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Publication number
WO2021022501A1
WO2021022501A1 PCT/CN2019/099544 CN2019099544W WO2021022501A1 WO 2021022501 A1 WO2021022501 A1 WO 2021022501A1 CN 2019099544 W CN2019099544 W CN 2019099544W WO 2021022501 A1 WO2021022501 A1 WO 2021022501A1
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micro
product
distribution
dot
feature
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PCT/CN2019/099544
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English (en)
French (fr)
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谢晖
高煜
闫钰龙
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罗伯特·博世有限公司
谢晖
高煜
闫钰龙
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Priority to DE112019007001.0T priority Critical patent/DE112019007001T5/de
Priority to PCT/CN2019/099544 priority patent/WO2021022501A1/zh
Priority to CN201980098985.7A priority patent/CN114175048A/zh
Publication of WO2021022501A1 publication Critical patent/WO2021022501A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
    • G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
    • G06K19/06009—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking
    • G06K19/06046—Constructional details
    • G06K19/06084—Constructional details the marking being based on nanoparticles or microbeads
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B42—BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
    • B42D—BOOKS; BOOK COVERS; LOOSE LEAVES; PRINTED MATTER CHARACTERISED BY IDENTIFICATION OR SECURITY FEATURES; PRINTED MATTER OF SPECIAL FORMAT OR STYLE NOT OTHERWISE PROVIDED FOR; DEVICES FOR USE THEREWITH AND NOT OTHERWISE PROVIDED FOR; MOVABLE-STRIP WRITING OR READING APPARATUS
    • B42D25/00—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof
    • B42D25/30—Identification or security features, e.g. for preventing forgery
    • B42D25/305—Associated digital information
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
    • G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
    • G06K19/08—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code using markings of different kinds or more than one marking of the same kind in the same record carrier, e.g. one marking being sensed by optical and the other by magnetic means
    • G06K19/083—Constructional details
    • G06K19/086—Constructional details with markings consisting of randomly placed or oriented elements, the randomness of the elements being useable for generating a unique identifying signature of the record carrier, e.g. randomly placed magnetic fibers or magnetic particles in the body of a credit card

Definitions

  • the invention relates to an anti-counterfeiting product and its manufacturing method and verification method.
  • Digital anti-counterfeiting technology uses barcodes or two-dimensional codes to give products a unique identification (ID) for anti-counterfeiting verification and traceability functions. This digital anti-counterfeiting technology is easy to be copied and has poor security.
  • Texture anti-counterfeiting technology uses randomly generated natural textures as anti-counterfeiting features. This texture is physically non-reproducible and has non-reproducible features; however, the existing texture anti-counterfeiting technology lacks automatic identification capabilities for anti-counterfeiting features, and automatic identification capabilities are required
  • the anti-counterfeiting features are visually identifiable, or rely on the addition of fiber materials in the production process to form the anti-counterfeiting features, which leads to an increase in the cost of anti-counterfeit products and inconvenience in production.
  • the purpose of the present invention is to provide an anti-counterfeiting product that not only has non-reproducible anti-counterfeiting features, but also can reduce production costs, and provides its manufacturing method and identification method to overcome the problems in the prior art.
  • the present invention provides an anti-counterfeiting product, including a product identification; wherein randomly distributed micro-dots are distributed on the surface of the product identification.
  • the product identification includes at least one of a barcode and a two-dimensional graphic code.
  • the micro-dots in the micro-dot features have at least one of a predetermined shape feature, a position feature, and a color feature.
  • the size of each micro-dot in the micro-dot feature is set in the range of 50 micrometers to 90 micrometers.
  • the present invention also provides a method for manufacturing an anti-counterfeiting product, including: generating randomly distributed micro-dot features; generating a digital product identification of the product; embedding the micro-dot feature into the digital product identification of the product; The digital product identification embedded with the micro-dot feature is printed on the surface of the product.
  • the micro-dots in the micro-dot features have at least one of a predetermined shape feature, a position feature, and a color feature.
  • generating random distribution of micro-point features includes: specifying a random distribution function and obtaining its probability density function PDF; obtaining the cumulative distribution function CDF of the PDF; performing inverse transformation on the CDF to obtain the inverse function CDF of the CDF -1; Generate a uniformly distributed random number U; and substitute U into CDF-1 to obtain a random distribution of micro-point features.
  • the random distribution of the micro-point features includes at least one of uniform distribution, Gaussian distribution, skewed Gaussian distribution, exponential distribution or a joint distribution of any combination thereof.
  • the method for manufacturing an anti-counterfeiting product further includes: sampling the obtained distribution of micro-dot features, and generating uniquely identifying micro-dot features for each product or product identification.
  • embedding the micro-dot feature into the digital product identification of the product includes: embedding the micro-dot feature into the digital product identification of the product according to an avoidance rule; wherein, the avoidance The rules restrict the position distribution and color distribution of the micro-dots to ensure that the micro-dots do not affect the normal reading of the product identification.
  • the method for manufacturing an anti-counterfeiting product further includes: saving the micro-dot features generated for each product for product verification.
  • the present invention also provides an anti-counterfeiting product verification method, including: acquiring an image of the product, and finding the area where the product identification of the product is located from the acquired image; and using image processing technology to read the area The micro-point features in the product; extract the pre-saved micro-dot features generated for the product; and compare the read micro-dot features with the pre-saved micro-dot features generated for the product to verify the authenticity of the product Pseudo.
  • the comparing step includes: judging whether at least one of the microscopic shape, position distribution, and color distribution of the read micro-dot features is consistent with the pre-stored micro-dot features generated for the product .
  • the technical effects produced include but are not limited to: by using the combination of micro-dot features and product identification, the conventional traceability function of one product-identity is preserved, and the micro-point feature is added.
  • Dot features are non-reproducible anti-counterfeiting features, thereby improving the security of the anti-counterfeiting system; at the same time, because the micro dot features are randomly distributed on the surface of the product identification, it is easy to generate the micro dot features during the printing process of the product identification, which can simplify The production process of anti-counterfeiting products and save costs.
  • Fig. 1 is a flowchart of a method for manufacturing an anti-counterfeiting product according to an embodiment of the present invention
  • Figure 2 is a schematic diagram of embedding micro-dot features into a product QR code in an embodiment
  • Figures 3(a) to 3(h) are examples of shape vectors of microdots in the embodiment.
  • Figures 4(a) and 4(b) show the image of the probability density function when the uniform distribution function is used as the random distribution function of the microdots, and the microdot distribution map obtained by sampling from the random distribution;
  • Figures 5(a) and 5(b) show an image of the probability density function when a one-dimensional Gaussian distribution function is used as the random distribution function of micropoints, and a micropoint distribution diagram sampled from the random distribution;
  • Fig. 6(a) and Fig. 6(b) show the image of the probability density function when the two-dimensional Gaussian distribution function is used as the random distribution function of the micropoints, and the distribution diagram of the micropoints sampled from the random distribution;
  • Figure 7 is a schematic diagram of a high-dimensional random distribution of micropoints in an embodiment
  • Fig. 8 is a flowchart of an anti-counterfeit product verification method according to an embodiment of the present invention.
  • Fig. 1 is a flowchart of a method for manufacturing an anti-counterfeiting product according to an embodiment of the present invention.
  • the product manufacturing method includes: generating randomly distributed micro-dot features (step 101); generating a digital product identification of the product (step 102); embedding the micro-dot feature into the digital product identification of the product (step 103); embedding the micro-dot
  • the characteristic digital product identification is printed on the surface of the product (step 104).
  • the surface of the product identification is distributed with randomly distributed micro-dot features, thereby completing the production of anti-counterfeiting products.
  • FIG. 2 is a schematic diagram of embedding the micro-dot feature into the product two-dimensional code in an embodiment, and the micro-dot feature 202 is not shown in detail in the figure due to its small size.
  • an algorithm is used to generate a specific high-dimensional random distribution map 201 of micro-points as the distribution characteristics of at least one of the location distribution, grayscale distribution, color distribution, and micro-morphology of all micro-point features.
  • Products in the same category or the same batch can follow a certain distribution characteristic, and each product has other different micro-point characteristics to show distinction. For example, different batches of products can use different random distribution maps, and different products of the same batch can use different microdots.
  • the algorithm is used to sample the random distribution map of the micro-dots, and a uniquely identifiable micro-dot feature 202 is generated for each product (or product identification or label), and then the generated micro-dot feature is combined according to a predetermined avoidance rule Embedded in the digital two-dimensional identification 203 of the product (such as quick response matrix code, that is, two-dimensional code), and print the two-dimensional code embedded with micro-dot features on the surface of the product or the surface of the product packaging as the product identification, or print Manufactured on the surface of the product label to form a digital product identification (ID) with micro-dots.
  • a predetermined avoidance rule Embedded in the digital two-dimensional identification 203 of the product (such as quick response matrix code, that is, two-dimensional code)
  • ID digital product identification
  • the avoidance rule can restrict at least one of the specific position distribution, grayscale distribution, and color distribution of the microdots.
  • the location distribution avoidance rule can ensure that only black or dark micro-dots are generated in the white module of the QR code, and the gray distribution or color distribution avoidance rule can distribute to ensure that the grayscale or color of the micro-dots meets a certain grayscale and saturation Degree limit, will not interfere with the white module of the QR code.
  • These evasion rules work together to ensure that the reading of the two-dimensional code itself will not be affected by the embedded micro-dot features, and make the two-dimensional code still meet the corresponding national standards and/or international standards after the micro-dot features are added. For the same type of products, they can have a common micro-dot distribution, but their micro-dots are different in details (such as microscopic morphology).
  • the micro-dot feature 202 can also be embedded in the black module of the two-dimensional code 203.
  • the avoidance rule restricts the micro-dots to be generated to the black module of the two-dimensional code, so that the two-dimensional code is added with the micro-dots.
  • the point feature still meets the corresponding national standards and/or international standards.
  • the white micro-dots maintain the highest contrast in the black module of the two-dimensional code, and the white micro-dots are produced by short pauses in the printing inkjet during the printing process.
  • the composition of the micro-point feature includes the most basic two-dimensional coordinates (X, Y), and can also include other optional features, such as color, grayscale, shape, and so on.
  • the non-reproducibility and anti-counterfeiting performance of micro-dots are first realized by the random distribution of the two-dimensional positions of micro-dots.
  • the color, grayscale or shape characteristics of the micro-dots can be used to further improve the anti-counterfeiting performance of the product.
  • Randomly distributed micro-dot features can also form randomly distributed micro-dot texture features.
  • the size of the microdots (for example, the diameter of the microdots or the longest diameter inside the microdots) can be set in the range of 50 microns to 90 microns, more preferably 60 microns to 80 microns In the range. Setting the size of the micro-dots within the above-mentioned range can make the micro-dots smaller than the size range that can be clearly identified by most sampling devices (such as high-resolution scanners) used in the market. If an ordinary sampling device is used to copy the two-dimensional code area covered with micro-dots, these micro-dots will be lost in the acquired sampled image. Counterfeiters can only copy the QR code on the product.
  • the counterfeiter uses a higher-performance scanner to copy the product logo with embedded micro-dot features, it will increase the cost of copying and spend more time. And because the micro-point feature is embedded in the QR code, counterfeit products can still be found through the traceability function of the QR code. Therefore, the entire anti-counterfeiting system has a high level of security.
  • the micro-point feature information on the product identification needs to be stored in the database for subsequent product authenticity verification.
  • the saved feature information of the micro-points includes, for example, randomly distributed location features and other features such as its color, gray scale, or shape.
  • Figures 3(a) to 3(h) are examples of the shape vectors of the microdots in the embodiment, in which eight types of microdots having different shapes (microscopic morphologies) are shown.
  • the lines inside the shape of each micro-dot are only exemplary and not necessary, and the color inside may be white, black or other colors.
  • the shapes of these micro-points illustrated in Figures 3(a) to 3(h) can be encoded as shape vectors shape 1 , shape 2 , shape 3 , shape 4 , shape 5 , shape 6 , shape 7 , and shape 8 , respectively.
  • the codes can be 000, 001, 010, 011, 100, 101, 110, 111 respectively.
  • FIGS. 3(a) to 3(h) are only exemplary, and the micro-dots may adopt other shapes in practical applications.
  • the characteristics of the micro-dots are sampled from the probability density function, that is, the location distribution of the micro-dot features or the micro-dot group in the product identification conforms to a predetermined random distribution.
  • This random distribution of micropoints includes at least one of uniform distribution, Gaussian distribution, skew Gaussian distribution, exponential distribution, or a joint distribution of any combination thereof.
  • 4(a) and 4(b) show the image of the probability density function when the uniform distribution function is used as the random distribution function of the microdots, and the microdot distribution map obtained by sampling from the random distribution.
  • the probability density function of the uniform distribution function is:
  • the Z coordinate is the probability density
  • the horizontal coordinate X and the vertical coordinate Y indicate the position (x, y) of the micro point.
  • the micro-point distribution map in Fig. 4(b) is sampled from the random distribution map in Fig. 4(a) when the micro-point coordinates (x, y) are generated.
  • Fig. 5(a) and Fig. 5(b) show the image of the probability density function when the one-dimensional Gaussian distribution function is used as the random distribution function of the microdots, and the microdot distribution map sampled from the random distribution.
  • the probability density function of one-dimensional Gaussian distribution is:
  • the Z coordinate is the probability density
  • the horizontal coordinate X and the vertical coordinate Y indicate the position (x, y) of the micro point.
  • the micro-point distribution map of Fig. 5(b) is obtained by sampling from the random distribution map of Fig. 5(a) when generating the micro-point coordinates (x, y).
  • FIG. 6(a) and FIG. 6(b) show the image of the probability density function when the two-dimensional Gaussian distribution function is used as the random distribution function of the microdots, and the microdot distribution map sampled from the random distribution.
  • the probability density function of the two-dimensional Gaussian distribution is:
  • the Z coordinate is the probability density
  • the horizontal coordinate X and the vertical coordinate Y indicate the position (x, y) of the micro point.
  • the micro-point distribution map of Fig. 6(b) is obtained by sampling from the random distribution map of Fig. 6(a) when generating the micro-point coordinates (x, y).
  • the inverse transform method can be used to generate the micro-point features. Specific steps are as follows:
  • Step 1 First specify a random distribution to obtain its probability density function PDF
  • Step 2 Find the cumulative distribution function CDF of PDF
  • Step 3 Perform inverse transformation on CDF to obtain its inverse function CDF -1 ;
  • Step 4 Generate a uniformly distributed random number U.
  • Step 5 Substitute U into CDF -1 to obtain the random distribution of the micro-point features, that is, the randomly distributed micro-point group.
  • the form of random distribution of micro-points includes, but is not limited to, one-dimensional and high-dimensional forms of uniform distribution, Gaussian distribution, skew Gaussian distribution, exponential distribution, etc., as well as joint distributions obtained by combining them with each other.
  • the micro-dots In the high-dimensional random distribution of micro-dots, the micro-dots not only have position changes, but also gray-scale changes.
  • the micro-point feature can conform to the high-dimensional random distribution diagram shown in Figure 7.
  • the three graphs from top to bottom in Figure 7 respectively show the probability density distribution of the micro-point X coordinate, Y coordinate and gray scale.
  • the vertical Y coordinate in the three graphs indicates the probability density
  • the horizontal X coordinate indicates the X coordinate, Y coordinate and gray value of the micro-points.
  • the range of gray value is 0 to 255.
  • Fig. 8 is a flowchart of an anti-counterfeit product verification method according to an embodiment of the present invention.
  • the product verification method includes: obtaining the image of the product, and finding the area where the product identification of the product is located from the obtained image (step 801); using image processing technology to read the micro-point features in the area (step 802); Extract the pre-saved micro-dot features generated for the product (step 803); and compare the read micro-dot features with the pre-saved micro-dot features generated for the product to verify the authenticity of the product (step 804).
  • a single image or a series of image frames of the product identification can be captured by a camera, and then the area where the two-dimensional code is located can be accurately found, and image processing technology can be used to read the micro-point features.
  • the two-dimensional code on the product identification serves as an indicator.
  • the two-dimensional code can be used to realize the traceability function of the product transportation chain, and the two-dimensional code can also be used to extract and obtain the micro-point features stored in the database during the product manufacturing process. recording.
  • the QR code area can be used to assist the algorithm to perform image calibration and normalization, so that different micro-point features can be compared.
  • microdots Once the microdots are acquired, it can be verified whether the location distribution, color distribution, and high-dimensional distribution characteristics of the microscopic morphology of the microdot features are consistent with the corresponding microdot features generated in advance for the product. To determine whether the product is genuine or counterfeit.
  • micro-dots in the embodiments of the present invention is the random position distribution of micro-dots, but other micro-dot features other than those described in this specification can also be added to enrich the anti-counterfeiting performance. , And enhance robustness. It is also possible to point-to-point comparison between the finer micro-dot features and the record information saved when the micro-dots are generated during the production process. If the comparison results are the same, you can be more confident that the verified product is genuine.
  • the product verification device may need to remotely read the pre-stored micro-point feature database during the verification process, and there will be additional communication overhead in the process, but it can be confirmed by remote verification The authenticity of the product.

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Abstract

本发明涉及一种具有随机微点特征的防伪产品及其制作方法和验证方法。该防伪产品包括产品标识，在产品标识的表面分布有随机分布的微点特征。该防伪产品的制作方法包括：生成随机分布的微点特征；生成产品的数字式产品标识；将微点特征嵌入到产品的数字式产品标识；将嵌入微点特征的数字式产品标识印制在产品的表面上。

Description

具有随机微点特征的防伪产品及其制作方法和验证方法 技术领域
本发明涉及一种防伪产品及其制作方法和验证方法。
背景技术
假冒伪劣产品对于生产者和消费都造成巨大的损失，因此需要通过使用安全可靠的防伪技术来加以控制。现有的针对产品的防伪技术包括：
数字防伪技术，利用条形码或二维码来给产品一个唯一的身份识别(ID)用于防伪验证和可追溯性功能，这种数字防伪技术易于被复制，且安全性差。
纹理防伪技术，使用随机生成的自然纹理作为防伪特征，这种纹理是物理不可复制的，具有不可再现的特征；但是现有的纹理防伪技术缺乏对防伪特征的自动鉴别能力，而自动鉴别能力要求防伪特征具有可视识别性，或者依赖于在生产过程中添加纤维材料来形成防伪特征，从而导致防伪产品的成本上升以及不便于生产。
发明内容
本发明的目的在于提供一种既具有不可再现的防伪特征、又可降低生产成本的防伪产品，并提供其制作方法和鉴别方法，以克服现有技术存在的问题。
本发明提供一种防伪产品，包括产品标识；其中，在所述产品标识的表面分布有随机分布的微点特征。
根据本发明的实施例，所述产品标识包括条形码和二维图形码中 的至少一种。
根据本发明的实施例，所述微点特征中的微点具有预定的形状特征、位置特征、颜色特征中的至少一项。
根据本发明的实施例，所述微点特征中的每个微点的尺寸被设置为50微米至90微米的范围内。
本发明还提供一种防伪产品的制作方法，包括：生成随机分布的微点特征；生成所述产品的数字式产品标识；将所述微点特征嵌入到所述产品的数字式产品标识；将嵌入所述微点特征的所述数字式产品标识印制在所述产品的表面上。
根据本发明的实施例，所述微点特征中的微点具有预定的形状特征、位置特征、颜色特征中的至少一项。
根据本发明的实施例，生成随机分布的微点特征包括：指定随机分布函数，并得到其概率密度函数PDF；求出PDF的累积分布函数CDF；对CDF进行逆变换，得到CDF的反函数CDF-1；生成均匀分布的随机数U；以及将U代入CDF-1中，得到微点特征的随机分布。
根据本发明的实施例，所述微点特征的随机分布包括均匀分布、高斯分布、偏态高斯分布、指数分布中的至少一种或其任意组合的联合分布。
根据本发明的实施例，防伪产品的制作方法还包括：对所得到的微点特征的分布进行采样，针对每个产品或产品标识生成具有唯一标识性的微点特征。
根据本发明的实施例，将所述微点特征嵌入到所述产品的数字式产品标识包括：根据回避规则将所述微点特征嵌入到所述产品的数字式产品标识；其中，所述回避规则限制所述微点的位置分布和颜色分布，以确保所述微点不影响所述产品标识的正常读取。
在本发明的一些实施例中，防伪产品的制作方法还包括：保存针对每个产品所生成的微点特征以用于产品验证。
本发明还提供一种防伪产品的验证方法，包括：获取所述产品的图像，并从所获取的图像中找到所述产品的产品标识所处的区域；利用图像处理技术来读取所述区域中的微点特征；提取预先保存的针对产品所生成的微点特征；以及将所读取的微点特征与预先保存的针对产品所生成的微点特征进行比较，以验证所述产品的真伪。
根据本发明的实施例，所述比较步骤包括：判断所读取的微点特征的微观形状、位置分布、颜色分布中的至少一项是否与预先保存的针对产品所生成的微点特征相一致。
根据本发明的实施例所提供的防伪产品，所产生的技术效果包括但不限于：通过采用微点特征与产品标识的组合，既保存了一产品一标识的常规可追溯功能，又增加了微点特征作为不可再现的防伪特征，从而改善了防伪体系的安全性；同时，由于微点特征随机分布在产品标识的表面，易于在产品标识的印制过程中生成微点特征，由此可以简化防伪产品的生产过程并节省成本。
附图说明
图1是根据本发明的实施例的防伪产品的制作方法流程图；
图2是在实施例中将微点特征嵌入到产品二维码中的示意图；
图3(a)至图3(h)是在实施例中的微点的形状向量的示例；
图4(a)和图4(b)示出采用均匀分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分布图；
图5(a)和图5(b)示出采用一维高斯分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分布图；
图6(a)和图6(b)示出采用二维高斯分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分 布图；
图7是在实施例中微点的高维随机分布示意图；
图8是根据本发明的实施例的防伪产品的验证方法流程图。
具体实施方式
以下结合符合附图和实施例进一步说明本发明。
图1是根据本发明的实施例的防伪产品的制作方法流程图。产品制作方法包括：生成随机分布的微点特征(步骤101)；生成产品的数字式产品标识(步骤102)；将微点特征嵌入到产品的数字式产品标识(步骤103)；将嵌入微点特征的数字式产品标识印制在产品的表面上(步骤104)。由此使得产品标识的表面分布有随机分布的微点特征，从而完成防伪产品的制作。
图2是在实施例中将微点特征嵌入到产品二维码中的示意图，其中的微点特征202因其尺寸太小而在图中未详细示出。在微点特征的生成过程中，首先通过算法生成微点的特定高维随机分布图201，作为所有微点特征的位置分布、灰度分布、颜色分布和微观形态中至少之一的分布特性，在相同类或相同批次的产品可以共同遵循某一分布特性，其中各个产品又具有其它不同的微点特征以示区分。例如，不同批次的产品可采用不同的随机分布图，相同批次的不同产品则采用不同的微点。然后，利用该算法对微点的随机分布图进行采样，针对每个产品(或产品标识或标签)生成具有唯一标识性的微点特征202，再根据预定的回避规则将所生成的微点特征嵌入到产品的数字二维标识203(如快速响应矩阵码，即二维码)中，并将嵌入有微点特征的二维码印制在产品表面或产品包装的表面作为产品标识，或印制在产品标签的表面上，形成具有微点的数字式产品标识(ID)。回避规则可以限制微点的特定位置分布、灰度分布和颜色分布中的至少一项。例如，位置分布回避规则可确保仅在二维码的白色模块中生成黑 色或深色微点，灰度分布或颜色分布回避规则可分布确保微点的灰度或颜色满足某种灰度和饱和度限制，不会干扰二维码的白色模块。这些回避规则共同作用而确保二维码本身的读取不会受到嵌入微点特征的影响，并且使得二维码在添加了微点特征之后仍然满足相应的国家标准和/或国际标准。针对相同类型的产品，它们可以具有共同的微点分布，但是它们的微点在细节(如微观形态)上是不同的。
在一些实施例中，也可以将微点特征202嵌入到二维码203的黑色模块中，回避规则将要生成的微点仅限制在二维码的黑色模块中，使得二维码在添加了微点特征之后仍然满足相应的国家标准和/或国际标准。白色微点在二维码的黑色模块中保持最高的对比度，并且在印制过程中通过印刷喷墨中的短停顿来产生白色微点。
微点特征的构成包括最基本的二维坐标(X,Y)，还可以包括其他可选特征，如颜色、灰度、形状等等。通常，微点特征的不可再现性和防伪性能首先是通过微点的二维位置的随机分布来实现。而微点的颜色、灰度或形状特征可用于进一步提高产品的防伪性能。随机分布微点特征也可以形成随机分布的微点纹理特征。
在本发明的实施例中，微点的尺寸(例如微点的直径或微点内部的最长径)可以被设置在50微米至90微米的范围内，更优选的是在60微米至80微米的范围内。将微点的尺寸设置在上述范围内，可以使得微点小于市场上使用的大多数采样设备(例如高分辨率扫描仪)所能清楚识别的尺寸范围。如果使用普通的采样设备复制覆盖有微点的二维码区域，则在所获取的采样图像中将会失去这些微点。仿冒者只能复制产品上的二维码。如果仿冒者使用更高性能的扫描仪进行复制嵌入有微点特征的产品标识，将会增加更高的复制成本及花费更多的时间。而且由于微点特征是嵌入在二维码中，因而仍然可以通过二维码的可追溯性功能来发现假冒产品。因此，整个防伪系统具有高等级的安全性。
在完成防伪产品的制作后或是在制作过程中，需要将产品标识上的微点特征信息保存数据库中，以用于后续的产品真伪验证。保存的微点特征信息例如包括随机分布的位置特征、以及如其颜色、灰度或形状等其它特征。
图3(a)至图3(h)是在实施例中的微点的形状向量的示例，其中示出八种具有不同形状(微观形态)的微点。每种微点的形状内部的线条仅是示例性的，不是必要的，其内部的颜色可以是白色、黑色或其它颜色。图3(a)至图3(h)图示的这些微点的形状可以分别被编码为形状向量shape 1、shape 2、shape 3、shape 4、shape 5、shape 6、shape 7、shape 8,，其编码可分别为000、001、010、011、100、101、110、111。例如，某个微点在产品标签上有位置坐标(X,Y)，其颜色为(R,G,B)＝(0,160,233)，形状为图3(f)所示的一个拐角形，假设这种形状被编码成了形状向量S＝shape 6，则该微点的特征可被描述为：(X,Y,R,G,B,S)。
本领域技术人员应该理解，图3(a)至图3(h)所示的微点的形状向量只是示例性的，在实际应用中的微点可以采用其它形状。
在本发明的实施例中，微点的特征采样于概率密度函数，也就是说，产品标识中的微点特征或微点群的位置分布符合预定的随机分布。微点的这种随机分布包括均匀分布、高斯分布、偏态高斯分布、指数分布中的至少一种或其任意组合的联合分布。
图4(a)和图4(b)示出采用均匀分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分布图。
均匀分布函数的概率密度函数为：
PDF(x,y)＝const
在图4(a)的概率密度函数的图像中，Z向坐标为概率密度，横向坐标X和纵向坐标Y指示微点的位置(x,y)。图4(b)的微点分布图是生成微点坐标(x,y)时从图4(a)的随机分布图中抽样得到的。
图5(a)和图5(b)示出采用一维高斯分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分布图。
一维高斯分布的概率密度函数为：
Figure PCTCN2019099544-appb-000001
在图5(a)的概率密度函数的图像中，Z向坐标为概率密度，横向坐标X和纵向坐标Y指示微点的位置(x,y)。图5(b)的微点分布图是生成微点坐标(x,y)时从图5(a)的随机分布图中抽样得到的。
图6(a)和图6(b)示出采用二维高斯分布函数作为微点的随机分布函数时的概率密度函数的图像、以及从随机分布中抽样得到的微点分布图。
二维高斯分布的概率密度函数为：
Figure PCTCN2019099544-appb-000002
在图6(a)的概率密度函数的图像中，Z向坐标为概率密度，横向坐标X和纵向坐标Y指示微点的位置(x,y)。图6(b)的微点分布图是生成微点坐标(x,y)时从图6(a)的随机分布图中抽样得到的。
在本发明的实施例中，可以采用逆变换法来生成微点特征。具体步骤如下：
步骤1：首先指定某一随机分布，得到其概率密度函数PDF；
步骤2：求PDF的累积分布函数CDF；
步骤3：对CDF进行逆变换，得到其反函数CDF -1；
步骤4：生成均匀分布的随机数U；以及
步骤5：将U代入CDF -1中，得到微点特征的随机分布，亦即随机分布的微点群。
微点随机分布的形式包括但不限于均匀分布、高斯分布、偏态高斯分布、指数分布等等的一维和高维形式，以及它们之间互相组合得 到的联合分布。在高维随机分布的微点特征中，微点不仅有位置变化，还可存在灰度上的变化。例如，微点特征可以符合图7所示的高维随机分布图，图7中从上至下的三个曲线图分别示出微点的X坐标、Y坐标和灰度的概率密度分布，这三个曲线图中的纵向Y坐标表示概率密度，横向X坐标分别指示微点的X坐标、Y坐标和灰度值。灰度值的范围为0至255。
图8是根据本发明的实施例的防伪产品的验证方法流程图。产品验证方法包括：获取产品的图像，并从所获取的图像中找到产品的产品标识所处的区域(步骤801)；利用图像处理技术来读取该区域中的微点特征(步骤802)；提取预先保存的针对产品所生成的微点特征(步骤803)；以及将所读取的微点特征与预先保存的针对产品所生成的微点特征进行比较，以验证该产品的真伪(步骤804)。
在验证过程中，例如可以通过相机摄取产品标识的单个图像或一系列图像帧，然后准确地找到二维码所处的区域，并利用图像处理技术来读取微点特征。产品标识上的二维码起着指标的作用，利用二维码可以实现产品运输链的可追溯功能，还可以利用二维码来提取并获得在产品制作过程中保存在数据库中的微点特征记录。二维码区域可以用于辅助算法执行图像校准和归一化，使得可以对不同的微点特征进行比较。一旦获取微点，就可以验证微点特征的位置分布、颜色分布和微观形态的高维度分布特性是否与预先保存的针对产品所生成的相应微点特征相一致。以确定是否产品是真的或假冒的。
本领域技术人员应该理解，在本发明的实施例中最基本的微点特征是微点的随机位置分布，然而还可以增加如本说明书所述之外的其它微点特征使其防伪性能得到充实，并且增强鲁棒性。也可以将更精细的微点特征与产品制作过程中生成微点时保存的记录信息进行点对点的比较。如果比较结果相同，就可以更加确信被验证产品是真的。根据本发明的实施例所实现的防伪系统，其产品验证设备在验证过程 由于可能需要远程读取预先保存的微点特征数据库，在其过程中将有附加的通信开销，但通过远程验证可以确认产品的真伪。
本领域技术人员应当理解，以上所描述的各个实施例只是说明性的，而非限制性的，本领域技术人员可以在不偏离本发明实质的情况下做出各种变形和修改，这些变形和修改都应落入本发明的保护范围之内。

Claims (13)

  1. 一种防伪产品，包括产品标识；其中，在所述产品标识的表面分布有随机分布的微点特征。
  2. 根据权利要求1的防伪产品，其中，所述产品标识包括条形码和二维图形码中的至少一种。
  3. 根据权利要求1的防伪产品，其中，所述微点特征中的微点具有预定的形状特征、位置特征、灰度特征、颜色特征中的至少一项。
  4. 根据权利要求1的防伪产品，其中，所述微点特征中的每个微点的尺寸被设置为50微米至90微米的范围内。
  5. 一种防伪产品的制作方法，包括：
    生成随机分布的微点特征；
    生成所述产品的数字式产品标识；
    将所述微点特征嵌入到所述产品的数字式产品标识；
    将嵌入所述微点特征的所述数字式产品标识印制在所述产品的表面上。
  6. 根据权利要求5的方法，其中，所述微点特征中的微点具有预定的形状特征、位置特征、灰度特征、颜色特征中的至少一项。
  7. 根据权利要求5的方法，其中，生成随机分布的微点特征包括：
    指定随机分布函数，并得到其概率密度函数PDF；
    求出PDF的累积分布函数CDF；
    对CDF进行逆变换，得到CDF的反函数CDF -1；
    生成均匀分布的随机数U；以及
    将U代入CDF -1中，得到微点特征的随机分布。
  8. 根据权利要求7的方法，其中，所述微点特征的随机分布包括均匀分布、高斯分布、偏态高斯分布、指数分布中的至少一种或其任意组合的联合分布。
  9. 根据权利要求7的方法，还包括：
    对所得到的微点特征的分布进行采样，针对每个产品或产品标识生成具有唯一标识性的微点特征。
  10. 根据权利要求5的方法，其中，将所述微点特征嵌入到所述产品的数字式产品标识包括：
    根据回避规则将所述微点特征嵌入到所述产品的数字式产品标识；其中，所述回避规则限制所述微点的位置分布、灰度分布和颜色分布中的至少一项，以确保所述微点不影响所述产品标识的正常读取。
  11. 根据权利要求5或9的方法，还包括：
    保存针对每个产品所生成的微点特征以用于产品验证。
  12. 一种防伪产品的验证方法，包括：
    获取所述产品的图像，并从所获取的图像中找到所述产品的产品标识所处的区域；
    利用图像处理技术来读取所述区域中的微点特征；
    提取预先保存的针对产品所生成的微点特征；以及
    将所读取的微点特征与预先保存的针对产品所生成的微点特征进行比较，以验证所述产品的真伪。
  13. 根据权利要求12的方法，其中，所述比较包括：
    判断所读取的微点特征的微观形状、位置分布、灰度分布、颜色分布中的至少一项是否与预先保存的针对产品所生成的微点特征相一致。
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