DE112019007001T5 - Anti-counterfeiting product with random microdot features and method of manufacture and verification thereof - Google Patents

Abstract

The present invention relates to an anti-counterfeiting product with random microdot features, and a manufacturing method and a verification method therefor. The anti-counterfeiting product includes a product identifier with randomly distributed microdot features distributed on a surface of the product identifier. The manufacturing method for the anti-counterfeiting product includes: generating randomly distributed microdot features; generating a digital product identifier of the product; embedding the microdot features into the product's digital identifier; and printing the digital product identifier of the product with the embedded microdot features on a surface of the product.

Description

technical field

The present invention relates to a counterfeit-proof product and a manufacturing method and a verification method therefor.

State of the art

• digitale Fälschungsschutztechnologien, bei denen Barcodes oder zweidimensionale Codes verwendet werden, um einem Produkt eine eindeutige Identifikation (ID) für die fälschungssichere Verifizierung und die Rückverfolgbarkeit zu verleihen, wobei jedoch diese digitale Fälschungsschutztechnologie leicht zu kopieren ist und eine schlechte Sicherheit aufweist.
• Fälschungsschutztechnologien mittels Texturen, bei den zufällig erzeugte natürliche Texturen als fälschungssichere Merkmale verwendet werden, wobei diese Texturen physikalisch nicht kopierbar und nicht reproduzierbar sind. Allerdings fehlt es den herkömmlichen Fälschungsschutztechnologien an der Fähigkeit, fälschungssichere Merkmale automatisch zu identifizieren. Die Fähigkeit zur automatischen Identifizierung erfordert, dass die fälschungssicheren Merkmale eine visuelle Erkennbarkeit aufweisen, oder sie ist auf die Zugabe eines Fasermaterials im Produktionsprozess angewiesen, um die fälschungssicheren Merkmale zu bilden, was zu einer Erhöhung der Kosten für die fälschungssicheren Produkte und zu Unannehmlichkeiten bei der Herstellung führt.

Counterfeit and substandard products cause huge losses for both manufacturers and consumers, which is why they must be countered by using safe and reliable anti-counterfeiting technologies. Traditional product anti-counterfeiting technologies include:

• digital anti-counterfeiting technology that uses barcodes or two-dimensional codes to give a product a unique identification (ID) for anti-counterfeiting verification and traceability, however, this digital anti-counterfeiting technology is easy to copy and has poor security.
• Texture-based anti-counterfeiting technologies that use randomly generated natural textures as anti-counterfeiting features, which textures are physically non-copyable and non-reproducible. However, traditional anti-counterfeiting technologies lack the ability to automatically identify anti-counterfeiting features. The automatic identification capability requires that the anti-counterfeiting features have visual detectability, or it relies on the addition of a fiber material in the production process to form the anti-counterfeiting features, resulting in an increase in the cost of the anti-counterfeiting products and inconvenience in the manufacturing leads.

Disclosure of Invention

The object of the present invention is to provide an anti-counterfeiting product which not only has non-reproducible anti-counterfeiting features but also can reduce the production cost, and a manufacturing method and an authentication method therefor to overcome the existing problems in the prior art.

The present invention provides an anti-counterfeiting product comprising a product identifier wherein randomly distributed microdot features are distributed on a surface of the product identifier.

According to an exemplary embodiment of the present invention, it is provided that the product identifier comprises at least one of a barcode and a two-dimensional graphic code.

According to an exemplary embodiment of the present invention, it is provided that a microdot has at least one of a predetermined shape feature, a position feature and a color feature in the microdot features.

According to an embodiment of the present invention, it is contemplated that the size of each microdot in the microdot features is set in the range of 50 microns to 90 microns.

The present invention further provides a manufacturing method for an anti-counterfeiting product, comprising: generating randomly distributed microdot features; generating a digital product identifier of the product; embedding the microdot features into the product's digital identifier; and printing the digital product identifier of the product with the embedded microdot features on a surface of the product.

According to an exemplary embodiment of the present invention, it is provided that a microdot has at least one of a predetermined shape feature, a position feature and a color feature in the microdot features.

According to an embodiment of the present invention, it is provided that the generation of randomly distributed micropoint features comprises: specifying a random distribution function and obtaining its probability density function PDF; determining a cumulative distribution function CDF of PDF; inverse transforming CDF to obtain an inverse function CDF-1 of CDF; generating a uniformly distributed random number U; Substitute U into CDF-1 to get a random distribution of microdot features.

According to an embodiment of the present invention, it is contemplated that the random distribution of microdot features comprises at least one of uniform distribution, Gaussian distribution, skewed Gaussian distribution, exponential distribution, or is a joint distribution of any combination thereof.

According to an embodiment of the present invention, it is envisaged that the manufacturing method for an anti-counterfeiting product further comprises: scanning the obtained distribution of the microdot features, and generating the microdot features with a unique identity for each product or product identifier.

According to an exemplary embodiment of the present invention, it is provided that embedding the microdot features in the digital product identifier of the product comprises: embedding the microdot features in the digital product identifier of the product according to an avoidance rule, the avoidance rule constraining a position distribution and a color distribution of the microdots to ensure that the microdots do not interfere with the intended reading of the product label.

In some embodiments of the present invention, it is envisaged that the manufacturing method for a counterfeit-proof product further comprises: storing the microdot features generated for each product for product verification.

The present invention further provides a verification method for an anti-counterfeiting product, comprising: obtaining an image of the product and finding an area where a product identifier of the product is located from the obtained image; reading microdot features in the area using an image processing technology; retrieving pre-stored microdot features generated for the product; and comparing the read microdot features to the pre-stored microdot features generated for products to verify the authenticity of the product.

According to an embodiment of the present invention, it is provided that the step of "comparing" comprises: determining whether at least one of the microscopic shape, position distribution and color distribution of the read microdot features is identical to the pre-stored microdot features generated for products .

The technical effects provided by the anti-counterfeiting product according to the exemplary embodiments of the present invention include, but are not limited to: The combination of microdot features and product identifiers not only makes the conventional function of traceability "an identifier to a product" maintained, but also improved the security of the anti-counterfeiting system by adding microdot features as non-reproducible anti-counterfeiting features. At the same time, since the microdot features are randomly distributed on the surface of the product tag, it is easy to generate the microdot features during the product tag printing process, whereby the production process of anti-counterfeiting products can be simplified and the cost can be saved.

character list

• 1 shows a flowchart of a manufacturing method for a counterfeit-proof product according to an embodiment of the present invention;
• 2 Figure 12 shows a schematic view for embedding microdot features in a two-dimensional code of the product according to an embodiment;
• 3(a) until 3(h) 12 show examples of shape vectors of microdots according to embodiments;
• 4(a) and 4(b) Fig. 12 shows an image of the probability density function when the uniform distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution;
• 5(a) and 5(b) Fig. 12 shows an image of the probability density function when the one-dimensional Gaussian distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution;
• 6(a) and 6(b) Fig. 12 shows an image of the probability density function when the two-dimensional Gaussian distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution;
• 7 shows a schematic view of a high-dimensional random distribution of microdots according to an embodiment;
• 8th shows a flowchart of a verification method for a counterfeit-proof product according to an embodiment of the present invention.

Concrete embodiments

Hereinafter, the present invention will be described in more detail in connection with the accompanying drawings and embodiments.

• Generieren von zufällig verteilten Mikropunkt-Merkmalen (Schritt 101); Generieren eines digitalen Produktkennzeichens des Produkts (Schritt 102); Einbetten der Mikropunkt-Merkmale in das digitale Produktkennzeichen des Produkts (Schritt 103); Drucken des digitalen Produktkennzeichens des Produkts mit den eingebetteten Mikropunkt-Merkmalen auf einer Oberfläche des Produkts (Schritt 104). Dadurch werden zufällig verteilte Mikropunkt-Merkmale auf einer Oberfläche des Produktkennzeichens verteilt, wodurch die Herstellung eines fälschungssicheren Produkts abgeschlossen wird.

1 shows a flowchart of a manufacturing method for a counterfeit-proof product according to an embodiment of the present invention. The manufacturing process for a counterfeit-proof product includes:

• generating randomly distributed microdot features (step 101); generating a digital product identifier of the product (step 102); embedding the microdot features into the product's digital product identifier (step 103); Printing the digital product identifier of the product with the embedded microdot features on a surface of the product (step 104). This distributes randomly distributed microdot features on a surface of the product identifier, thereby completing the manufacture of a counterfeit-proof product.

2 FIG. 12 shows a schematic view for embedding microdot features in a two-dimensional code of the product according to an embodiment, wherein the microdot features 202 are not shown in detail in the figure due to their too small size. During the generation of microdot features, an algorithm is first used to generate a specific high-dimensional random distribution diagram 201 of microdots, which serves as the distribution properties of at least one of position distribution, grayscale distribution, color distribution, and micromorphology of all microdot features, where products of the same category or the same batch may share a particular distribution characteristic, with the individual products further having different microdot features to distinguish them. For example, different random distribution plots can be used for products from different batches, while different microdots are used for different products from the same batch. The algorithm is then used to sample the random distribution plots of microdots, generating for each product (or tag) a microdot feature 202 with a unique identity, after which the generated microdot feature is converted into a digital product tag 203 according to an avoidance rule ( e.g., a quick-response matrix code, i.e., two-dimensional code) of the product is embedded, and wherein the two-dimensional code with the embedded microdot feature is printed on a surface of the product or a surface of the product packaging as a product identifier, or on a surface of the product label is printed to form a digital product identifier (ID) with microdots. The avoidance rule may restrict at least one of specific position distribution, gray level distribution, and color distribution of the microdots. For example, the avoidance rule for the position distribution can ensure that black or dark microdots are only generated in the white module of the two-dimensional code. The avoidance rule for the gray level distribution or color distribution can ensure that the gray levels or the colors of the microdots meet certain gray level and saturation limits and the white modulus of the two-dimensional code is not disturbed. These avoidance rules work together to ensure that the reading of the two-dimensional code itself is not affected by the embedded microdot feature and that after the addition of the microdot feature, the two-dimensional code still meets the appropriate national and/or international standards. For the same type of products, they may share a common microdot distribution, but their microdots differ in details (e.g. micromorphology).

In some embodiments, the microdot feature 202 may also be embedded in the black modulus of the two-dimensional code 203, where the avoidance rule limits the microdots to be generated to the black modulus of the two-dimensional code, so that after adding the microdot feature, the two-dimensional code corresponding national and/or international standards. The white microdots retain the highest contrast in the black module of the two-dimensional code, and the white microdots are created by brief pauses in the ink jet during the printing process.

The composition of a microdot feature includes the most basic two-dimensional coordinates (X,Y) and may also include other optional properties such as color, grayscale, shape, and so on. As a rule, the non-reproducibility and counterfeit security of the microdots are first realized by the random distribution of the two-dimensional positions of the microdots. The color, grayscale, or shape properties of the microdots can be used to further enhance the product's anti-counterfeiting properties. The randomly distributed microdot features can also form randomly distributed microdot texture features.

In the embodiments of the present invention, the size of the microdot (e.g., the diameter of the microdot or the longest diameter within the microdot) can be adjusted in the range of 50 microns to 90 microns, more preferably 60 microns to 80 microns. By setting the size of the microdot to the above range, the microdots can be smaller than the size range that can be clearly detected by most scanning devices (e.g. a high resolution scanner) used on the market. If a current scanning device is used to copy the two-dimensional code area covered with microdots, these microdots will be lost in the captured scanned image. A counterfeiter can only copy the two-dimensional code on the product. If the counterfeiter uses a more powerful scanner to copy the product identifier with embedded microdot features, this increases copying costs and time. Since the microdot feature is embedded in the two-dimensional code, counterfeit products can still be detected via the two-dimensional code's traceability function. Therefore, the entire anti-counterfeiting system has high security.

Upon completion of the production of the anti-counterfeiting product or during its production process, the microdot feature information in the product identifier must be stored in the database for subsequent verification of the authenticity of the product. The stored microdot feature information includes, for example, features of the randomly distributed positions and other features such as color, gray scale, or shape.

3(a) until 3(h) 12 show examples of shape vectors of microdots according to embodiments, showing eight microdots with different shapes (micromorphologies). The lines within the shape of each microdot are exemplary only and not necessary, and the color within the shape of each microdot can be white, black, or other colors. In the 3(a) until 3(h) The microdot shapes shown can be encoded as shape vectors shape ₁ , shape ₂ , shape ₃ , shape ₄ , shape ₅ , shape ₆ , shape ₇ and shape ₈ , respectively, and their codes can be 000, 001, 010, 011, 100, 101 , 110, 111. As an example, a specific microdot is given position coordinates (X,Y) on its product label, its color is (R,G,B)=(0,160,233), and its shape is an in 3(f) L-shape shown. Assuming that this shape is encoded as shape vector S = shape ₆ , the features of the microdot can be described as follows: (X, Y, R, G, B, S).

Those skilled in the art should understand that the shape vectors of the in 3(a) until 3(h) microdots shown are exemplary only and the microdots may take other forms in practical applications.

In the embodiment of the present invention, the features of the micropoint are sampled in the probability density function. That is, the positional distribution of the microdot feature or group of microdots in the product identifier conforms to a predetermined random distribution. This random distribution of microdot features includes at least one of uniform distribution, Gaussian distribution, skewed Gaussian distribution, exponential distribution, or is a joint distribution of any combination thereof.

4(a) and 4(b) Fig. 12 shows an image of the probability density function when the uniform distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution.

The probability density function of the uniform distribution function is: PDF(x, y) = const.

In the picture of the probability density function in 4(a) the Z-coordinate represents the probability density, where the horizontal coordinate X and the vertical coordinate Y indicate the position (x,y) of the microdot. The microdot distribution diagram in 4(b) becomes from the random distribution plot in 4(a) sampled when generating the coordinates (x,y) for the microdot.

5(a) and 5(b) Fig. 12 shows an image of the probability density function when the one-dimensional Gaussian distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution.

The probability density function of the one-dimensional Gaussian distribution function is: $$\text{PDF}\left(x,y\right)=\frac{1}{\sqrt{2\pi }\sigma }{e}^{-\frac{{\left(x-\mu \right)}^2}{2{\sigma }^2}}$$

In the picture of the probability density function in 5(a) the Z-coordinate represents the probability density, where the horizontal coordinate X and the vertical coordinate Y indicate the position (x,y) of the microdot. The microdot distribution diagram in 5(b) becomes from the random distribution plot in 5(a) sampled when generating the coordinates (x,y) for the microdot.

6(a) and 6(b) Fig. 12 shows an image of the probability density function when the two-dimensional Gaussian distribution function is used as the random distribution function of the microdots and a microdot distribution chart obtained by sampling the random distribution.

The probability density function of the two-dimensional Gaussian distribution function is: $$\text{PDF}\left(x,y\right)=\frac{1}{2\pi {\sigma }_1{\sigma }_2\sqrt{1-{\rho }^2}}{e}^{-\frac{1}{2\left(1-{\rho }^2\right)}\left[{\left(\frac{x-{\mu }_1}{{\sigma }_1}\right)}^2-2\rho \left(\frac{x-{\mu }_1}{{\sigma }_1}\right)\left(\frac{y-{\mu }_2}{2}\right)+{\left(\frac{y-{\mu }_2}{{\sigma }_2}\right)}^2\right]}$$

In the picture of the probability density function in 6(a) the Z-coordinate represents the probability density, where the horizontal coordinate X and the vertical coordinate Y indicate the position (x,y) of the microdot. The microdot distribution diagram in 6(b) becomes from the random distribution plot in 6(a) sampled when generating the (x,y) coordinates for the micropoint.

• Schritt 1: Angeben einer bestimmten zufälligen Verteilung und Erhalten ihrer Wahrscheinlichkeitsdichtefunktion PDF;
• Schritt 2: Ermitteln einer kumulativen Verteilungsfunktion CDF von PDF;
• Schritt 3: Inverses Transformieren von CDF, um ihre Umkehrfunktion CDF^-1 zu erhalten;
• Schritt 4: Generieren einer gleichmäßig verteilten Zufallszahl U; und
• Schritt 5: Einsetzen von U in CDF^-1, um eine zufällige Verteilung der Mikropunkt-Merkmale, d.h., zufällig verteilte Mikropunkt-Merkmale, zu erhalten.

In the embodiments of the present invention, the inverse transform may be used to generate microdot features. The concrete steps are as follows:

• Step 1: Specify a specific random distribution and obtain its probability density function PDF;
• Step 2: Find a cumulative distribution function CDF of PDF;
• Step 3: inverse transform CDF to obtain its inverse function CDF ^-1 ;
• Step 4: generate a uniformly distributed random number U; and
• Step 5: Substituting U into CDF ^-1 to obtain a random distribution of microdot features, ie, randomly distributed microdot features.

The shape of the random distribution of microdots includes, but is not limited to, one-dimensional and high-dimensional shapes such as a uniform distribution, a Gaussian distribution, a skewed Gaussian distribution, an exponential distribution, and the joint distribution obtained by combining them. In high-dimensional randomized microdot features, the microdot features are not only changed in position but also in gray level. For example, the microdot features can 7 correspond to the shown high-dimensional random distribution diagrams chen. The three diagrams shown from top to bottom in 7 are shown show the X-coordinate, Y-coordinate and probability density distribution of the gray level of the microdots, respectively. The vertical y-coordinate in these three graphs represents the probability density, and the horizontal x-coordinate represents the x-coordinate, y-coordinate and gray level of the microdots. The gray value ranges from 0 to 255.

8th shows a flowchart of a verification method for a counterfeit-proof product according to an embodiment of the present invention. The verification method includes: obtaining an image of the product and finding an area where a product identifier of the product is located from the obtained image (step 801); reading microdot features in the area using an image processing technology (step 802); extracting pre-stored microdot features generated for products (step 803); and comparing the read microdot features to the prestored microdot features generated for products to verify the authenticity of the product (step 804).

For example, in the verification process, a single image or a series of images of the product identifier can be captured by a camera, after which the area where the two-dimensional code is located can be found out precisely, reading the microdot features using the image processing technology. The two-dimensional code on the product label serves as an indicator. The two-dimensional code can enable the traceability function of the product transport chain. Furthermore, the two-dimensional code can also be used to retrieve and maintain records of the microdot features stored in the database during the product manufacturing process. The two-dimensional code domain can be used to help the algorithm calibrate and normalize the image so that different microdot features can be compared. Once the microdots are obtained, it can be verified that the position distribution, color distribution, and high-dimensional distribution properties of the micromorphology are identical to the pre-stored microdot features generated for products. This makes it possible to determine whether the product is authentic or counterfeit.

Those skilled in the art should understand that the most basic microdot feature in the embodiments of the present invention is the random positional distribution of microdots, but microdot features other than the microdot features described herein can be added to improve anti-counterfeiting and robustness raise. It is also possible to make a point-to-point comparison between the finer microdot features and the record information stored when the microdots are generated during the product production process. If the comparison result says "same", it can be more certain that the verified product is authentic. In the anti-counterfeiting system realized by the embodiments of the present invention, during the verification process, the product verification device may need to remotely read the pre-stored database of microdot features, resulting in an additional communication overhead. However, the authenticity of the product can be confirmed through remote verification.

Those skilled in the art should understand that the various embodiments described above are only illustrative and not restrictive. Various changes and modifications can be made by those skilled in the art without departing from the spirit of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims (13)

A counterfeit-proof product comprising a product identifier, wherein randomly distributed microdot features are distributed on a surface of the product identifier. Anti-counterfeit product claim 1 , wherein the product identifier comprises at least one of a bar code and a two-dimensional graphic code. Anti-counterfeit product claim 1 , wherein the microdots in the microdot features have at least one of a predetermined shape feature, a position feature, a grayscale feature, and a color feature. Anti-counterfeit product claim 1 , wherein the size of each microdot in the microdot features is set in the range of 50 microns to 90 microns. Manufacturing process for a counterfeit-proof product, comprising: generating randomly distributed microdot features; generating a digital product identifier of the product; embedding the microdot features into the product's digital identifier; and Printing the digital product identifier of the product with the embedded microdot features on a surface of the product. procedure after claim 5 , wherein the microdots in the microdot features have at least one of a predetermined shape feature, a position feature, a grayscale feature, and a color feature. procedure after claim 5 , wherein generating randomly distributed micropoint features comprises: specifying a random distribution function and obtaining its probability density function PDF; determining a cumulative distribution function CDF of PDF; inverse transforming CDF to obtain an inverse function CDF ^-1 of CDF; generating a uniformly distributed random number U; and substituting U into CDF ^-1 to obtain a random distribution of microdot features. procedure after claim 7 , wherein the random distribution of microdot features comprises at least one of uniform distribution, Gaussian distribution, skewed Gaussian distribution, exponential distribution, or is a joint distribution of any combination thereof. procedure after claim 7 , further comprising: scanning the obtained distribution of microdot features, and generating the microdot features with a unique identity for each product or product identifier. procedure after claim 5 , wherein embedding the microdot features in the product digital identifier comprises: embedding the microdot features in the product digital identifier according to an avoidance rule, the avoidance rule constraining at least one of positional distribution, grayscale distribution, and color distribution of the microdots to ensure that the microdots do not interfere with the intended reading of the product label. procedure after claim 5 or 9 , further comprising: storing the product verification microdot features generated for each product. Verification method for a counterfeit-proof product, comprising: obtaining an image of the product and finding an area where a product identifier of the product is located from the obtained image; Reading microdot features in the area using a image processing technology; retrieving pre-stored microdot features generated for the product; and Comparing the read microdot features with the pre-stored microdot features generated for the product to verify the authenticity of the product. procedure after claim 12 , wherein the comparing comprises: determining whether at least one of the microscopic shape, position distribution, gray scale distribution, and color distribution of the read microdot features is identical to the pre-stored microdot features generated for the product.

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