Anti-counterfeiting label, forming method thereof, anti-counterfeiting method and printed matter

Abstract

The present invention relates to an anti-counterfeiting label and a method for forming the same, an anti-counterfeiting method, and a printed matter. The anti-counterfeiting label comprises a functional graphic and a coding area with a random texture, wherein the functional graphic comprises at least one calibration graphic for distortion and / or clarity judgment; binary coding information is provided at a preset sampling point of each code element in the coding area, and a random texture image is provided at a non-preset sampling point of each code element. The present invention realizes the integration of a two-dimensional code and an anti-counterfeiting graphic at the most basic code element level of the two-dimensional code, wherein each code element contains both binary coding information and anti-counterfeiting texture information, thereby improving the anti-counterfeiting effect of the existing two-dimensional code; at the same time, by setting a calibration graphic, the distortion and clarity of the anti-counterfeiting label are judged before the anti-counterfeiting verification, thereby avoiding the situation where the anti-counterfeiting label cannot be identified due to insufficient clarity or distortion, thereby improving the verification and identification efficiency of the anti-counterfeiting label.

Description

Technical Field

[0001] The present invention relates to the field of two-dimensional code anti-counterfeiting, and in particular to an anti-counterfeiting label and a forming method thereof, an anti-counterfeiting method and a printed matter. Background Art

[0002] QR codes have been widely used in our daily lives. The current QR codes themselves are easy to imitate and have low anti-counterfeiting capabilities. Some QR codes with anti-counterfeiting functions simply combine the QR code and anti-counterfeiting graphics, without achieving a deep fusion of the QR code and the anti-counterfeiting graphics. At the same time, when using QR codes, it is necessary to use smart devices to automatically identify the anti-counterfeiting graphics of the QR code. The finer the anti-counterfeiting graphics, the better the anti-counterfeiting effect, but the higher the requirements for the clarity and distortion of the captured image, which leads to common situations where users have difficulty in identification and limited application scenarios. For example, the sealing of goods such as bottle caps is the best place to apply anti-counterfeiting labels, but these places are mostly irregular surfaces, curved surfaces, etc., and the captured images are bound to have distortion and clarity problems, making existing QR codes difficult to identify, limiting the application of texture anti-counterfeiting technology on curved surfaces. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an anti-counterfeiting label and a forming method thereof, an anti-counterfeiting method and a printed matter.

[0004] In a first aspect, an embodiment of the present invention provides an anti-counterfeiting label, comprising a functional graphic and a coding area with a random texture.

[0005] The functional graph includes at least one calibration graph for distortion and / or clarity judgment;

[0006] A preset sampling point of each code element in the coding area is provided with binary coding information, and a non-preset sampling point of each code element is provided with a random texture image.

[0007] The present invention has the following beneficial effects: the present invention designs an anti-counterfeiting label, which realizes the integration of the QR code and the anti-counterfeiting graphic at the most basic code element level of the QR code. Each code element contains both binary coding information and anti-counterfeiting texture information, thereby improving the anti-counterfeiting effect of the existing QR code; at the same time, by setting a calibration graphic, the distortion and clarity of the anti-counterfeiting label are judged before the anti-counterfeiting verification, thereby avoiding the situation where the anti-counterfeiting label cannot be recognized due to insufficient clarity or distortion, and improving the verification and recognition efficiency of the anti-counterfeiting label.

[0008] Furthermore, the calibration pattern is composed of at least one group of measurement units, each group of measurement units includes a pair of adjacent black and white patterns with the same width, and the black and white patterns of two adjacent groups of measurement units are alternately and concentrically distributed.

[0009] Furthermore, when the calibration pattern includes multiple groups of measurement units, the widths of the black and white patterns in different measurement units are different.

[0010] Furthermore, the maximum width of the black and white patterns in all measurement units is the width of the code element, and the minimum width of the black and white patterns in the measurement units is the minimum printing width of the adopted printing device.

[0011] Furthermore, the calibration figures include one or more of concentric circles, concentric semicircles, concentric sectors, concentric squares, concentric rectangles and concentric polygons.

[0012] Furthermore, the calibration graphic is arranged at the center of the anti-counterfeiting label and / or the calibration graphic is arranged at the edge of the anti-counterfeiting label.

[0013] In a second aspect, the present invention provides a printed matter having any of the above anti-counterfeiting labels printed thereon.

[0014] In a third aspect, the present invention provides a method for forming the anti-counterfeiting label described above, comprising the following steps:

[0015] Step 11, converting the target information into a target codeword sequence according to the QR code encoding rules;

[0016] Step 12: prefabricate a two-dimensional code array including functional graphics, and set a coding area in the two-dimensional code array, wherein the functional graphics include at least one calibration graphic for distortion and / or clarity judgment;

[0017] Step 13, determining the information drawing position of each code element in the coding area according to the preset sampling point;

[0018] Step 14: Fill the information drawing position of each symbol in the coding area with color according to the target codeword sequence, and form a random texture image at the non-preset sampling point of each symbol.

[0019] In a fourth aspect, the present invention provides an anti-counterfeiting method for the anti-counterfeiting label described above, comprising the following steps:

[0020] Step 21: When printing and producing the anti-counterfeiting label, a first image of the anti-counterfeiting label is captured, and a calibration pattern of the first image is extracted;

[0021] Step 22: detecting the clarity of the calibration image, and when the clarity meets a preset condition, extracting binary coded information of the first image;

[0022] Step 23: When the binary coded information is consistent with the target codeword sequence corresponding to the anti-counterfeiting label, the clarity of the calibration pattern is saved as the target clarity, and feature point information in the random texture image of the anti-counterfeiting label is extracted and saved as texture sample features;

[0023] Step 24, capturing a second image of the anti-counterfeiting label to be identified, identifying a calibration pattern of the second image, and dividing the anti-counterfeiting label to be identified into a plurality of regions based on the calibration pattern;

[0024] Step 25: Detect the clarity of the calibration pattern corresponding to each area and compare it with the target clarity. Adjust the position and angle of the acquisition device according to the comparison result until the calibration pattern of each area meets the preset clarity qualification condition.

[0025] Step 26: extracting actual random texture image features of feature points in the anti-counterfeiting label to be identified, matching the actual random texture image features with corresponding texture sample features, and verifying the authenticity of the anti-counterfeiting label based on the matching results.

[0026] Furthermore, the calibration pattern in the anti-counterfeiting label to be identified includes a first measurement unit and a second measurement unit, and the width of the black and white pattern in the first measurement unit is greater than the width of the black and white pattern in the second measurement unit;

[0027] The clarity includes contrast and resolution. The contrast of the calibration figure is characterized by the brightness ratio of the first measurement unit on any radial direction in the calibration figure corresponding to each area; the resolution of the calibration figure is characterized by the brightness ratio of the second measurement unit on any radial direction in the calibration figure corresponding to each area.

[0028] In order to make the above-mentioned objects, features and advantages of the invention more obvious and easy to understand, preferred embodiments of the present invention are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of a traditional QR code;

[0030] Figure 2 A schematic diagram of an anti-counterfeiting label provided in Example 1;

[0031] Figure 3 A schematic flow chart of a method for forming an anti-counterfeiting label provided in Example 2;

[0032] Figure 4 A schematic diagram of the internal structure of the anti-counterfeiting label provided in Example 2;

[0033] Figure 5A schematic flow chart of an anti-counterfeiting method using an anti-counterfeiting label provided in Example 3;

[0034] Figure 6 This is a schematic diagram of the area division and radial direction in the anti-counterfeiting method provided in Example 3. DETAILED DESCRIPTION

[0035] In the following description, specific details such as particular system structures, interfaces, and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention can be practiced in alternative embodiments without these specific details. In other cases, detailed descriptions of well-known systems, circuits, and methods are omitted to avoid obscuring the description of the present invention with unnecessary detail.

[0036] To facilitate understanding, let’s first introduce traditional QR codes (such as quick response, QR code).

[0037] Traditional QR codes are typically arranged in a two-dimensional rectangular area and are composed of many small basic units. These small basic units are called code elements. Code elements are the basic units that form a QR code, and QR codes are generally composed of many code elements spliced ​​together or aggregated.

[0038] A common code element shape is a square, and is colored black and white. However, it should be noted that embodiments of the present invention are not limited thereto. For example, the code element shape can be a square, a circle, a rounded square, or a combination of these shapes. The code element color can be, for example, a combination of black and white, where black represents a binary 1 and white represents a binary 0. Alternatively, the code element color can be a combination of red and white, where red represents a binary 1 and white represents a binary 0. Of course, QR codes can also use other color combinations, as long as the color combination can be recognized and distinguished by a machine.

[0039] Traditional QR codes come in multiple versions, each with a different number of code elements. Consequently, the information capacity of each version also varies. For example, version 1 contains 21 x 21 code elements, version 2 contains 25 x 25 code elements, and so on, up to version 40, which contains 177 x 177 code elements. The higher the version, the more code elements the QR code contains, and the more information it can hold.

[0040] Figure 1 This is the structure diagram of the traditional QR code, such as Figure 1As shown in the figure, the traditional QR code includes a functional graphic and a coding area 7, with a blank area 3 around the coding area 7. The functional graphic also includes a position detection graphic 5, a positioning graphic 4, a correction graphic 6, etc. The position detection graphic 5 can be used to determine the direction of the QR code. Traditional QR codes generally include three position detection graphics, which are respectively distributed in the upper left corner, upper right corner and lower left corner of the QR code. The position detection graphics are generally patterns with fixed proportions, such as Figure 1 As shown, it is a black and white "U"-shaped pattern, and the ratio of black and white code elements is 1:1:3:1:1. In the process of scanning the QR code, the position detection pattern of the QR code is generally searched based on this fixed ratio to determine the direction of the QR code.

[0041] like Figure 1 As shown, the traditional QR code's positioning pattern 4 includes horizontal and vertical positioning patterns, each one module wide, consisting of alternating dark and light modules, with a dark module at the beginning and end. Typically, the horizontal and vertical positioning patterns are located in the sixth row and sixth column, respectively (row and column counting starts at 0), avoiding the position detection pattern. They determine the density and version of the symbol, provide a reference position for determining module coordinates, and aid alignment.

[0042] like Figure 1 As shown, a traditional QR code may also include one or more correction patterns 6, also known as auxiliary positioning patterns. It should be noted that not all versions of QR codes require correction patterns; generally, only versions 2 and above require them. The correction patterns are primarily used to determine whether the QR code is folded or distorted, and to correct the QR code if it is folded or distorted.

[0043] The encoding area 7 of the traditional QR code is mainly used to store the specification information and code words of the QR code. The specification information may include Figure 1 At least one of the format information 8 and version information 9 shown. The type of specification information mainly depends on the version of the QR code. Different versions of QR codes can be configured with different types of specification information. Taking the specification information including format information 8 and version information 9 as an example, the format information and version information are generally stored in Figure 1 The version information of the QR code can be used to indicate the size of the QR code (or the number of code elements in the QR code). The format information of the QR code is generally used to store some formatting data, such as the error correction level and mask information of the QR code. Figure 1 , Figure 1The code elements in the gray area are primarily used to record or store codewords. A codeword is a bit sequence obtained by binary encoding the target information. Codewords can include data codes and error correction codes. Common encoding methods for QR codes include digital encoding and character encoding. Error correction codes are generally calculated based on the selected error correction level using an error correction algorithm such as Reed-Solomon.

[0044] After determining the QR code's version information, format, and codeword, the code elements in the coded area can be filled with color according to preset rules. If code elements for functional graphics such as position detection patterns, positioning patterns, and correction patterns are encountered along the way, they can be bypassed or skipped. Alternatively, a preset mask pattern can be used to mask the filled QR code pattern, resulting in a more uniform color distribution in the resulting QR code.

[0045] Figure 2 A schematic diagram of an anti-counterfeiting label provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the anti-counterfeiting label complies with the national standard of QR code, and divides the anti-counterfeiting label into several unit grids, each grid being a code element, that is, the anti-counterfeiting label also includes a functional graphic and a coding area 7, and there is a blank area 3 around the coding area 7. Unlike ordinary two-dimensional code elements, each code element in the coding area in this embodiment contains not only binary data information but also random texture information for anti-counterfeiting. Specifically, binary coding information is provided at the preset sampling point of each code element, and random texture images for anti-counterfeiting are provided at the remaining positions of each code element, that is, non-preset sampling points. At the same time, in addition to the position detection graphic 5, positioning graphic 4 and correction graphic 6 mentioned above, the functional graphics of the anti-counterfeiting label of this embodiment also include at least one calibration graphic for distortion and / or clarity judgment during the anti-counterfeiting process, such as Figure 2 As shown, it includes a first calibration figure 2 and a second calibration figure 1. The above functional figures constitute a reference system that includes positioning, alignment, distortion, and clarity. The acquisition device can obtain information about the position, distortion, clarity, etc. of the acquired image by detecting this reference system in the acquired anti-counterfeiting label image, and judge whether the acquired image meets the standard based on this.

[0046] In order to judge the distortion and / or clarity, the calibration graphics are generally composed of multiple groups of concentric patterns of alternating high grayscale and low grayscale (for the convenience of expression below, black represents low grayscale 0 and white represents high grayscale 255), such as concentric circles, concentric semicircles, concentric sectors, concentric squares and concentric polygons, etc. An anti-counterfeiting label can contain multiple calibration graphics with different concentric patterns, and the calibration graphics are distributed at different positions within the coding area of ​​the anti-counterfeiting label or at the edge of the coding area. Figure 2 The illustration is presented using concentric circles and concentric squares. The concentric circle calibration pattern is located at the center of the anti-counterfeiting label, overlapping with the coding area at that location, while the concentric square calibration pattern is located at the edge of the anti-counterfeiting label. Of course, in other embodiments, the calibration patterns may also be of other numbers or shapes.

[0047] At the same time, each calibration figure may include one or more groups of measurement units, each group of measurement units includes a pair of adjacent black and white concentric patterns with the same width, and the black and white concentric patterns of two adjacent groups of measurement units are alternately and concentrically distributed. Figure 2 As shown, the anti-counterfeiting label includes a first calibration pattern in the form of concentric circles at the center and a second calibration pattern in the form of concentric squares at the edge. The first calibration pattern 2 includes a set of measurement units, as shown in 2a and 2b, and the second calibration pattern 1 includes a set of measurement units, as shown in 1a and 1b. The widths of the multiple groups of measurement units in a calibration pattern can vary, and the color sequence of the calibration pattern can be black-white-black-white or white-black-black-black, but they must alternate between two groups, that is, black and white.

[0048] As mentioned above, the calibration pattern is primarily used to indicate distortion and clarity. Specifically, under normal circumstances, the distance from the center of the calibration pattern to any edge point of the black pattern in each measurement unit should be equal, and the distance from the center of the calibration pattern to any edge point of the white pattern in each measurement unit should be equal. Deformation will cause the distance from the center to the edge to change.

[0049] First, when printing and producing the anti-counterfeiting label, the binary coded data on the label is read to obtain design information such as the shape, size, position, and number of the calibration pattern. This allows the distance from any edge point to the center of the calibration pattern to be calculated, and this distance is used as the distortion verification distance. An image of the anti-counterfeiting label to be authenticated is then captured, the calibration pattern in the image is identified, and the actual distance from any edge point to the center of the calibration pattern is measured. The difference between the actual distance and the distortion verification distance is calculated to determine the magnitude and direction of the distortion on the label.

[0050] At the same time, the clarity of the anti-counterfeiting label can also be detected through the calibration graphic. The various data encoding graphics represented by QR codes in the existing technology have increasingly higher data density per unit area with the evolution of versions. In order to accurately decode, the clarity requirements for the captured image are also increased. The various current data encoding graphics mostly use various image algorithms to restore image clarity. The finer the texture, the better the anti-counterfeiting effect, but the higher the clarity requirements for the captured image. The various current texture anti-counterfeiting technologies all use various image algorithms to restore image clarity, but the algorithm correction has a limit value. This embodiment sets a calibration graphic with a clarity indication function in the functional graphic. When capturing the image, the clarity of the captured anti-counterfeiting label image is evaluated by detecting and calculating the clarity value of this calibration graphic, so that the captured anti-counterfeiting label image meets the algorithm's requirements for image clarity. The specific anti-counterfeiting method of performing clarity detection on the anti-counterfeiting label through the above calibration graphic and performing random texture comparison after the clarity detection is described in detail in the following embodiments and will not be expanded here.

[0051] In a preferred embodiment, the number and position of the calibration patterns and the width of the black and white patterns in the measurement unit can be set according to the accuracy requirements of the clarity sampling in the anti-counterfeiting process. Specifically, when the clarity sampling requirements are not high, that is, in normal use, such as Figure 2 As shown, a circular calibration pattern is set in the center of the anti-counterfeiting label. The calibration pattern has a size of 5*5 grid units, and polygonal calibration patterns, such as rectangular calibration patterns, are set on the edge of the anti-counterfeiting label. Of course, the position, size, and number of calibration patterns are not limited to the above and can also be set according to actual conditions.

[0052] The width of the black and white pattern within the measurement unit can also be set based on anti-counterfeiting requirements. Different products have different anti-counterfeiting requirements, resulting in varying sizes, shapes, and texture thicknesses. Rigidly specifying the width of the measurement unit will likely fail to achieve the desired effect. A more appropriate approach is to select an appropriate black and white pattern width within a range based on actual usage. Specifically, both artificial and natural textures are composed of lines, dots, and blocks, all of which have a certain width. From the perspective of texture creation, finer and denser lines are more difficult to create. Alternatively, the finer and denser the texture, the more difficult and costly it is to counterfeit. To improve anti-counterfeiting effectiveness and detection efficiency, the width of the calibration pattern is limited within a certain range. The maximum width cannot exceed the code element width, otherwise it will affect binary information reading. The minimum width cannot be less than the maximum resolution of the printing device, i.e., the minimum printing width. For example, a 300dpi printer can theoretically only print lines as thin as 0.08mm; anything thinner is impossible.

[0053] In actual anti-counterfeiting applications, the shape characteristics of lines within a certain width range of the calibration pattern are selected for detection based on the customer's anti-counterfeiting accuracy requirements. Therefore, within the above calibration pattern width range, a more appropriate calibration pattern width can be set based on the anti-counterfeiting accuracy requirements. The specific steps include:

[0054] First, a mapping table is established, which includes the correspondence between different security levels and detection line widths. For example, in one embodiment, a 1200dpi printer is selected. Since the maximum fineness of a 1200dpi printer is 0.02, 0.02-1 mm is divided into 10 security levels from high to low for manufacturers to choose from. Of course, in other embodiments, the security level and corresponding detection line width can also be set according to the manufacturer's requirements.

[0055] Then, the target security level is obtained, and the mapping relationship table is queried to obtain the detection line width corresponding to the target security level. For example, if the user selects the target security level as level 5, the corresponding detection line width obtained after querying the mapping relationship table is 0.49 mm.

[0056] Finally, the test line width is scaled down and up according to a preset ratio to create the target width range for the calibration pattern. For example, if the left and right intervals are scaled by 1 / 2, the calibration pattern width range will be 0.25 mm and 0.74 mm for a test line width of 0.49 mm. This allows the calibration pattern to be produced within this width range.

[0057] A second embodiment of the present invention provides a printed article having the anti-counterfeiting label described above printed thereon. This embodiment of the present invention does not specifically limit the material or printing technique of the printed article. For example, the printed article can be made of one or more of paper, plastic, and metal. The printed article can be printed using one or more of mimeograph, lead printing, and offset printing techniques.

[0058] Embodiment 2 of the present invention provides a method for forming the anti-counterfeiting label as described above, such as Figure 3 As shown, the following steps are included:

[0059] Step 11: Convert the target information into a target codeword sequence according to the QR code encoding rules. The specific encoding method is described in detail in the prior art and will not be described in this embodiment.

[0060] Step 12: Prefabricate a QR code array containing functional graphics and set a coding area within the QR code array. The functional graphics include at least one calibration pattern for distortion and / or clarity determination. In these steps, the various functional graphics described above, such as the positioning pattern, the locating pattern, the correction pattern, and the calibration pattern, are drawn into the QR code array, followed by the version information and format information.

[0061] Step 13, determining the information drawing position of each code element in the coding area based on the preset sampling points. Specifically, in order to improve the decoding speed during decoding, the QR code decoding software can sample at certain points within the grid instead of reading the entire grid, that is, all the points of the entire code element. Therefore, when forming the anti-counterfeiting label, the information drawing position of each code element can also be determined based on the preset sampling points, so that only the binary data information is drawn at the information drawing position. The number, size and position of the preset sampling points here are not specified in the QR code national standard. Therefore, sampling can be performed at the center point of the code element, or the sampling points can be arranged as needed. For example, in one embodiment, three-point sampling can be adopted, that is, three preset sampling points are set in each code element, such as setting a preset sampling point at the upper left 1 / 3, the center, and the lower right 1 / 3 of the code element.

[0062] Then, step 14 is executed to fill in the color at the information drawing position of each code element in the coding area according to the target code word sequence, and form a random texture image at the remaining position of each code element, that is, at the non-preset sampling point. Specifically, this solution first converts the information that you want to be scanned and displayed into a target code word sequence according to the QR code encoding rules, and becomes individual code elements. Then, according to the sampling point information of the decoding software, binary data information (dark and light dots) is set at the corresponding position in each code element, that is, the sampling point position. Random functions are used at other positions in the code element, and random textures are produced by using the random infiltration and diffusion of ink. In this way, a code element contains both binary coding information and random anti-counterfeiting texture information, realizing the integration of anti-counterfeiting and QR code. Figure 4 At the same time, the random texture formed by this scheme contains lines of varying widths and lengths, and spans a large range, from micrometers to millimeters, making it difficult to replicate.

[0063] The preferred embodiment also provides two specific methods for forming the above random textures. One is to first generate a dot-shaped QR code, generate a texture image of corresponding size using a random function according to the size of the QR code image, and then superimpose the QR code image on the texture image to form the anti-counterfeiting label.

[0064] Another approach uses the edge of a symbol and the edge of a preset sampling point as the boundary. Starting from the edge of the sampling point, a random function is used to randomly draw a black and white pattern in this area until the area is completely filled. If the preset sampling point is a white point, the random value is biased towards white, and vice versa. This method creates a more natural random texture, without the sudden appearance of a white point in a black field.

[0065] In practical applications, the above two solutions can also be combined, that is, first use the first solution to make an initial image, and then use the second solution to correct the random texture image around the preset sampling point, thereby taking into account both efficiency and natural beauty.

[0066] Embodiment 3 of the present invention provides an anti-counterfeiting method using the anti-counterfeiting label described above, such as Figure 5 As shown, the following steps are included:

[0067] Step 21, when printing and producing the anti-counterfeiting label, the first image of the anti-counterfeiting label is collected by the collection device, and the calibration pattern of the first image is extracted. When the anti-counterfeiting label is designed and produced according to the manufacturer's requirements, the shape, size, coordinate position, etc. of the calibration pattern will be recorded in the remote network database, and an index number will be generated. At the same time, the corresponding random texture data will also be stored under this index number, and this index number will also be written into the binary coded information in the anti-counterfeiting label. When reading the first image of the anti-counterfeiting label, its binary coded information is first read to obtain the index number. According to the index number, the corresponding record of the remote network database is called, and then the pattern recognition algorithm is called accordingly to extract the calibration pattern in the first image.

[0068] Step 22: The acquisition device detects the clarity of the calibration image, and when the clarity meets a preset condition, extracts the binary coding information of the first image.

[0069] Specifically, after optically imaging a group of closely adjacent graphics of the same width and with large grayscale contrast, the image can be compared with the original image. At this time, the brightness and darkness have changed, with the bright parts becoming darker and the dark parts becoming brighter, and the entire image becoming gray. Therefore, by detecting the brightness of the image and comparing it with the brightness of the original image, the image clarity can be determined. In optical imaging, clarity is measured by contrast and resolution. In this embodiment, the calibration graphic is provided with various measurement units with different black and white pattern widths. The measurement units with large black and white pattern widths in the calibration graphic are used to indicate contrast because the black and white contrast is more intense, and the measurement units with small black and white pattern widths are used to indicate resolution because details can be seen. Specifically, the brightness ratio of the measurement units with large black and white pattern widths is detected to determine the contrast of the calibration graphic, and the brightness ratio of the measurement units with small black and white pattern widths is detected to determine the resolution of the calibration graphic.

[0070] Here, the clarity value is represented by the brightness ratio of a measurement unit in at least one radial direction in any calibration figure. The radial direction is a virtual ray used to describe the position and direction information of the clarity sampling point. In this embodiment, there are two description expressions. The first is that the calibration figure adopts a concentric circular pattern. At this time, an xy virtual coordinate system parallel to the grid coordinate system UV is established with the center of the calibration figure as the origin. A virtual ray pointing from the origin to the edge direction of the calibration figure is called a radial direction. The description method is the angle with the x-axis, that is, a certain angle radial direction or a certain arc radial direction. For example, a 30-degree radial direction refers to a virtual ray that makes an angle of 30 degrees with the x-axis. The second is that the calibration figure adopts a concentric square pattern, and when the calibration figure is set at the edge, the radial direction is from the point outside the grid plane in the calibration figure to the center of the grid plane, and is represented by the coordinate value of this point in the UV coordinate system, such as Figure 6 shown.

[0071] Then calculate the brightness ratio of a measurement unit in any radial direction. The brightness ratio is calculated as follows: V a -V b / V a +V b , where V a is the maximum brightness of the black and white pattern in a set of measurement units in any radial direction, V b The minimum brightness of the black and white pattern in this measurement unit. The introduction of the radial method here increases the data acquisition density per unit area and provides more extended functions. For example, by collecting clarity values ​​in different radial directions and combining them with specific AI algorithms, data such as image noise and the lighting conditions of the acquisition environment can be indirectly analyzed. In the above embodiment, clarity is measured by calculating the brightness ratio. When the brightness ratio reaches a preset value, the clarity condition is met, and then the binary encoded information of the first image is extracted.

[0072] Then, step 23 is executed to determine whether the binary coded information is consistent with the target code sequence corresponding to the anti-counterfeiting label. If they are consistent, it indicates that the printed QR code information is correct. At this time, the clarity of the calibration graphic is saved as the target clarity (i.e., the target brightness ratio), and the feature point information of the random texture image within the detection range of the anti-counterfeiting label is extracted and saved, such as the texture image of feature points such as corner points and edges as texture sample features. Specifically, the target clarity and texture sample features can be saved in a cloud database.

[0073] Then, when the user needs to scan the anti-counterfeiting label, the anti-counterfeiting label becomes the anti-counterfeiting label to be identified. At this time, step 24 is executed to collect the second image of the anti-counterfeiting label to be identified through the acquisition device. The acquisition device first reads the binary coded information of the anti-counterfeiting label to be identified in the same way as a conventional QR code, and obtains the relevant design information of the calibration pattern therein. Then, the calibration pattern of the second image is identified after pre-processing the second image, and the anti-counterfeiting label to be identified is divided into multiple areas based on the calibration pattern, such as Figure 6 As shown, the circular calibration pattern at the center divides the security label to be identified into four equal areas. If multiple calibration patterns are identified in the second image, the center points of each calibration pattern are connected by a horizontal line parallel to the X-axis and a vertical line parallel to the Y-axis, thereby dividing the security label to be identified into multiple areas.

[0074] Step 25: Detect the clarity of the calibration pattern corresponding to each region and compare it with the target clarity. Based on the comparison results, adjust the position and angle of the acquisition device until the preset clarity qualification is achieved. The data for each region is independent, and each region has a corresponding partial calibration pattern. The distortion and clarity of that partial calibration pattern are then measured to measure the distortion and clarity of that region.

[0075] The method of calculating the distortion and clarity of the calibration image has been described in detail above and will not be expanded here. Figure 6 For example, the brightness ratios on the six radial directions of c1, c2, s1, s2, s3, and s4 can be detected, and then the brightness ratios can be compared with the target brightness ratios stored in the cloud database. If the image distortion and clarity requirements of the algorithm are met, the qualified block images are saved, and another thread is used to perform random texture comparison and judgment. The clarity deviation value of the unqualified block image is then calculated, and the user is guided to adjust the position and angle of the acquisition device accordingly until the clarity and image distortion of each block are qualified. In a preferred embodiment, because the illumination, acquisition angle, etc. are in a controllable and optimized state during sampling during the production process, the brightness ratio of a measurement unit on any radial direction can be selected as the target brightness ratio, or the average of the brightness ratios of the measurement units on multiple radial directions can be calculated as the target brightness ratio. You can also divide the anti-counterfeiting label into different blocks first, and calculate the target brightness ratio of the calibration graphics corresponding to different blocks respectively. For example, if the average brightness ratio of the 2b group of measurement units in the calibration graphics is 0.8, the 2b group of sampling values ​​on the c1\c2 radial direction is compared with 0.8.

[0076] In a preferred embodiment, the gyroscope information of the current acquisition device is first recorded. The collected calibration pattern is then compared with the standard calibration pattern to determine the direction and degree of distortion. By sampling and testing different points in the collected calibration patterns (e.g., points c1 and s1), the clarity and illumination distribution of the entire anti-counterfeiting label can be determined. This data can be used to estimate the approximate angle and direction of movement. After the acquisition device moves to the estimated position, the gyroscope information is recorded again. By repeating these steps, the next adjustment direction is determined, and the difference between the two gyroscope recordings is used to correct the direction until each block meets the required clarity and image distortion requirements.

[0077] Finally, step 26 is executed to extract the actual random texture image features of the feature points in the anti-counterfeiting label to be identified, match the actual random texture image features with the texture sample features, and verify the authenticity of the anti-counterfeiting label based on the matching results. In a specific embodiment, the number n of feature points collected and the threshold T are set according to the anti-counterfeiting accuracy. For example, in this embodiment, n takes a value of 40 and T takes a value of 0.5, that is, 10 feature points are randomly sampled in each of the 4 blocks, and the texture sample features of the corresponding feature points stored in the cloud are obtained based on the coordinate values ​​of these feature points. If more than 20 matches are successful, the actual threshold value T = 20 / 40 is calculated to be not less than 0.5, and the anti-counterfeiting label can be judged to be true. If not all blocks have been collected, and the number of feature points that match has reached or exceeded 20, the collection is stopped and it is judged to be true.

[0078] The present invention designs an anti-counterfeiting label and a formation method thereof, an anti-counterfeiting method and a printed matter, which realizes the integration of the QR code and the anti-counterfeiting graphic at the most basic code element level of the QR code. Each code element contains both binary coding information and anti-counterfeiting texture information, thereby improving the anti-counterfeiting effect of the existing QR code; at the same time, by setting a calibration graphic, the distortion and clarity of the anti-counterfeiting label are judged before the anti-counterfeiting verification, thereby avoiding the situation where the anti-counterfeiting label cannot be recognized due to insufficient clarity or distortion, and improving the verification and recognition efficiency of the anti-counterfeiting label.

[0079] The reader should understand that in the description of this specification, reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0080] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used, such as combining or integrating multiple units or components into another system, or ignoring or not implementing certain features.

[0082] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention.

[0083] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0084] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0085] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and such modifications or substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. An anti-counterfeiting label, characterized in that: The anti-counterfeiting label comprises a functional pattern and a coding area with a random texture, wherein the functional pattern includes at least one calibration pattern for distortion and / or clarity judgment; binary coding information is provided at a preset sampling point of each code element in the coding area, and a random texture image is provided at a non-preset sampling point of each code element; the anti-counterfeiting label is formed by the following steps: Step 11, converting the target information into a target codeword sequence according to the QR code encoding rules; Step 12: prefabricate a two-dimensional code array including functional graphics, and set a coding area in the two-dimensional code array, wherein the functional graphics include at least one calibration graphic for distortion and / or clarity judgment; Step 13, determining the information drawing position of each code element in the coding area according to the preset sampling point; Step 14, color-filling the information drawing position of each code element in the coding area according to the target code word sequence, and forming a random texture image at a non-preset sampling point of each code element; The calibration pattern is composed of at least one group of measurement units, each group of measurement units includes a pair of adjacent black and white patterns of the same width, and the black and white patterns of two adjacent groups of measurement units are alternately and concentrically distributed; when the calibration pattern includes multiple groups of measurement units, the widths of the black and white patterns of different measurement units are different, and the number and position of the calibration pattern and the width of the black and white patterns in the measurement units are set according to the accuracy requirements of the clarity sampling.

2. The anti-counterfeiting label according to claim 1, characterized in that: The maximum width of the black and white patterns in all measurement units is the width of the code element, and the minimum width of the black and white patterns in the measurement unit is the minimum printing width of the adopted printing device.

3. The anti-counterfeiting label according to claim 1, characterized in that: The calibration figures include one or more of concentric circles, concentric semicircles, concentric sectors, concentric squares, concentric rectangles and concentric polygons.

4. The anti-counterfeiting label according to claim 1, characterized in that: The calibration graphic is arranged at the center of the anti-counterfeiting label and / or the calibration graphic is arranged at the edge of the anti-counterfeiting label.

5. A printed matter, characterized in that The printed matter is printed with the anti-counterfeiting label according to any one of claims 1 to 4.

6. An anti-counterfeiting method, using the anti-counterfeiting label according to any one of claims 1 to 4, characterized in that: The following steps are involved: Step 21: When printing and producing the anti-counterfeiting label, a first image of the anti-counterfeiting label is captured, and a calibration pattern of the first image is extracted; Step 22: detecting the clarity of the calibration image, and when the clarity meets a preset condition, extracting binary coded information of the first image; Step 23: When the binary coded information is consistent with the target codeword sequence corresponding to the anti-counterfeiting label, the clarity of the calibration pattern is saved as the target clarity, and feature point information in the random texture image of the anti-counterfeiting label is extracted and saved as texture sample features; Step 24, capturing a second image of the anti-counterfeiting label to be identified, identifying a calibration pattern of the second image, and dividing the anti-counterfeiting label to be identified into a plurality of regions based on the calibration pattern; Step 25: Detect the clarity of the calibration pattern corresponding to each area and compare it with the target clarity. Adjust the position and angle of the acquisition device according to the comparison result until the calibration pattern of each area meets the preset clarity qualification condition. Step 26: extracting actual random texture image features of feature points in the anti-counterfeiting label to be identified, matching the actual random texture image features with corresponding texture sample features, and verifying the authenticity of the anti-counterfeiting label based on the matching results.

7. The anti-counterfeiting method according to claim 6, characterized in that: The calibration pattern in the anti-counterfeiting label to be identified includes a first measurement unit and a second measurement unit, and the width of the black and white pattern in the first measurement unit is greater than the width of the black and white pattern in the second measurement unit; The clarity includes contrast and resolution. The contrast of the calibration figure is characterized by the brightness ratio of the first measurement unit on any radial direction in the calibration figure corresponding to each area, and the resolution of the calibration figure is characterized by the brightness ratio of the second measurement unit on any radial direction in the calibration figure corresponding to each area.

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