Method for printing and identifying authentication marks with amplitude-modulated raster printing

Abstract

Printing method and authentication method for a print (33) to be created of a digital image, comprising printing an authentication mark by applying an amplitude-modulated raster to an object in a detection area (21), wherein the printed face of the detection area is composed of asymmetric raster dots (8), wherein at least two mutually non-parallel framing edges (211, 212) in at least one framing area (190) are printed to determine the position, delimitation and orientation of the detection area, and a method for authenticating such a print (33), comprising providing an image recording device for carrying out the authentication procedure, providing a derived printed image from the printed object for a predetermined number of raster dots in the detection area (21) pre-determined from the print data, and providing a computer program for comparing the printed image pre-determined from the raster dot data, wherein the method comprises recording an image of the printed object, identifying the at least two framing edges for exactly determining the detection area from the image in the raster dots, comparing the recorded printed image of the detection area with the derived printed image, and deciding on the basis of the comparison whether an original print is present on the object.

Description

Technical Field

[0001] This invention relates to a printing method and an authentication method for a printout to be created for a digital image, comprising: a method for printing an authentication mark by applying at least amplitude-modulated raster printing to a probe area onto an object, wherein the printed surface of the probe area comprises adjacent raster units, in which raster dots in a matrix composed of printable raster elements are printed respectively. The invention also relates to the verification of original printouts manufactured by means of this raster printing method. Background Technology

[0002] A major part of counterfeiting worldwide involves the copying and reproduction of printed documents and packaging. This includes not only government ID documents such as passports and identity cards, but also documents proving the originality of commercial products. This includes certificates, accompanying documents, certificates of origin, and, to a large extent, packaging for branded products. The widespread distribution of a product, i.e., its market size and the expected profits for counterfeiters, are motivating factors. Correspondingly, well-known brands, especially those with a promise of high quality and thus high final selling or retail prices, become targets of counterfeiting. In effect, all industrial branches within the consumer and industrial goods sectors are affected; known examples include spare parts for private cars, watches, and pharmaceuticals. In principle, all types of packaging are affected, such as blister packs, cardboard packaging, rigid packaging (cans, etc.), especially packaging whose design can be replicated using printing methods such as offset printing, flexographic printing, or digital printing. Here, the quality of the counterfeit packaging is good, even very good in some respects; a good counterfeit should be understood as one that does not immediately attract the attention of consumers or service staff, but only when directly compared with the original. Very good counterfeits are only visible to trained professionals or even revealed through legal inspection during targeted investigations. The convincing replication of packaging designs and other documents belonging to the original product becomes easy with the help of easy-to-use, high-performance scanners and visual elements on the packaging that are generally easily identifiable or convey proof of the product's originality. Precise implementations of icons in their color and geometry, the functionality of barcodes, or counterfeit serial numbers pose no obstacle to counterfeiters. Considering the fact that counterfeit products in the end-consumer market often circulate only days after a new product's release, this demonstrates both the efficiency of organized counterfeiting crimes and the still very inadequate measures used to protect branded products. Therefore, there is a need for robust and reliable verification of the original printed packaging and documents, at a reasonable cost. Regarding the cost of verification, legal laboratory inspections are, for example, unreasonable. More precisely, original manufacturers, industrial customers, and consumers demand rapid verification through ubiquitous means, often necessitating verification via smartphones and appropriate applications (Apps).

[0003] Digital watermarking can be used to some extent as a form of copy protection, although the primary purpose of the watermark is to protect information embedded in an image. Whenever the message (“content”) embedded in an object, such as an image, needs to be extracted, a password or similar measure is required. Secure and reliable extraction of the message requires measures that have the opposite effect. Therefore, corrective coding, for example, for redundant extraction of embedded information, is a potential entry point for hackers. Digital watermarking does not necessarily achieve pure copy protection in the sense of original identification (Copy Detection); it is especially impractical if the original printout consists of a photographic image whose quality cannot be degraded by integrated protection measures. In contrast, additionally embedded information is secondary, even if advantageous in some applications. Some examples of digital watermarking include using a biconvex lens-like structure on the data carrier (US10'065'441B1), changing the hue by gradually altering the amount of color (US 10'127'623B1), replacing special colors (such as Pantone) with the basic colors of a color system (such as CMYK) (US10'270'936B1), or other types of adjustments to the printed image that are visible to the design upon further observation.

[0004] In principle, original documents can be identified using digital fingerprinting because copies of original printouts always differ from the originals to a small degree, as long as they are not so-called complete counterfeits (so-called third-shift or night-shift counterfeits) manufactured by the manufacturer or a packaging service provider or printer certified by said manufacturer. This is due to the flow of printing ink, ink absorption through the paper used, etc. The usual "content fingerprint" of object characteristics is not particularly robust and has a high error rate. Additionally, original document identification via digital fingerprinting requires significant IT resources, thus resulting in a relatively slow verification process. Using additional features, such as combining a printed timestamp with a serial number placed on the packaging, can largely eliminate complete counterfeits. This additional feature is more suitable for investigating and verifying originality in a second step.

[0005] EP 3 686 027 A1 describes a method for printing an authentication mark onto an object by applying at least amplitude-modulated grating printing in a detection area. The method uses adjacent grating units, in which grating dots are printed from a matrix of printable grating elements, wherein each hue value of the grating print corresponds to a grating plane of a grating peak for the grating dot. Here, in the detection area, for multiple hue values ​​of the grating dots to be printed, the associated grating plane of the grating peak is modified in a predetermined manner such that, while maintaining the same printed hue values, a predetermined matrix image of the grating elements to be printed is assigned to the grating plane.

[0006] DE10 2018 115146A1 relates to a method for creating a security element in an image that is invisible to the human eye and cannot be copied, particularly for verifying the authenticity of an image, wherein the image is formed by means of a printed grating, wherein the printed grating is composed of individual image points. At least one field is defined in the printed grating, wherein uncopyable encrypted information is stored by means of manipulation of the image points in the field and / or by means of manipulation of the entire field for comparison with at least one database. Thus, the image has at least one uncopyable security element, wherein the image has evaluable information within its printed grating, such that the image has at least one field having manipulation of the image points invisible to the human eye and / or an invisible manipulated field. Here, changes to the grating are achieved, for example, by: exchanging grating angles between two or more colors, changing the grating angle of at least one color, changing the running width or grating frequency of the line grating of at least one color, changing the frequency or amplitude in the case of a frequency-modulated grating of at least one color, or changing the amplitude or frequency in the case of an amplitude-modulated grating of at least one color. Summary of the Invention

[0007] Based on the aforementioned existing technology, there is a need for a relatively simple printing method and a downstream copy detection method, the method

[0008] ●Does not compromise the quality of images captured on documents or packaging.

[0009] ●Image elements that are hidden from the naked eye for use in original document identification

[0010] ●Also suitable for color images,

[0011] ● It can be performed using a smartphone.

[0012] ● Avoid or eliminate unnecessary expenses (stands, lighting, long waiting times, complicated operations).

[0013] The objective is achieved by means of the raster printing method of claim 1.

[0014] Known methods for reading information, such as QR codes, require at least one viewing area in addition to at least one detection area containing the information to be read. The presence, location, and orientation of the detection area can be determined by one or more of these viewing areas. As in EAN scanners or QR codes, this can occur partly through user guidance, whereby the user holds a recording device that captures the information from a support, causing the entire code area to be recorded. Subsequently, in the QR code, the orientation of the surface containing the information is determined by predetermined markings. Now, the object of the present invention is to provide, in addition to concealing the information on the original document, one or more viewing areas necessary to locate the information, such that these viewing areas are also inconspicuous to the naked eye, but conversely, are identifiable by an automated machine.

[0015] It is also important here that the framing area is not necessarily located at the edge of the image. Yes, this is precisely part of the invention, and the edge of the image, even if or only a transition to a white, in this case, unprinted edge area of ​​the packaging, is not included in the final determination of the framing area because, by definition, the white surface does not have detectable raster points.

[0016] The invention presented herein achieves this objective by displaying image elements with selected raster dot shapes. The solution follows the fact that the printed material of a digital template is altered through the printing process itself, during which deviations can be identified at a microscopic level. For example, printing ink is not precisely distributed in the image medium within a space predetermined by the recorder elements (the smallest printing elements, referred to as Rels). The size of the individually controllable exposure element is the exposure pixel. The size of the exposure pixel is derived from the exposure unit resolution and corresponds to the diameter of the laser dot; the higher the exposure unit resolution, the smaller the Rel.

[0017] The structure of the medium (paper, paperboard, coated paperboard) and the flow characteristics of the printing ink facilitate the process of widening and deforming the raster dots. Scanning and further scanning-based printing introduce further blurring into the printed image of the copy, which, with a suitable digital template, can be distinguishably differentiated from the original print in such a way that image detection devices, such as smartphone cameras with suitable software, can accurately distinguish the copy from the original print. Of particular interest is that suitable microscopic elements are not added to the image as independent graphics, but rather as part of the image construction. In this respect, it is suitable to replace standard circular, oval, or elliptical raster dots with raster dots having more pronounced shapes. For example, circular raster dots do not significantly change their shape during printing; conversely, U-shaped raster dots, such as those in… Figure 1A As shown, or L-shaped grating point 4, as in Figure 1B As shown, with the same number of recorder elements being printed, the printed images 2 or 5 appear slightly different microscopically. A copy of the original print is shown here on the right. Figure 1A or Figure 1B The third image shows shapes 3 or 6, again with the same raster dots, shapes almost indistinguishable from U or L. It is noteworthy that the difference in the shape of the raster dots avoids the viewer's eye as long as the raster dots do not change the size of their faces, and thus the halftone values ​​they represent. In other words, a "good" copy of the original print created by the raster printing method performed according to the invention has the same grayscale values ​​and appears identical to the naked eye. The same applies to color prints, in which predetermined colors of four ink layers, typically applied at different raster angles, are printed using the method according to the invention. Typically, the color chosen for this is the top or second-top layer color, i.e., the last or penultimate ink layer printed.

[0018] Conversely, objects composed of raster dots are transferred in the copy with a superficially similar quality relative to the original, as illustrated by the transformation of the appearance of the digital template marked 1 or 4 in the original print 2 or 5, or in a copy 3 or 6 of the original print. Part of the invention is to propose a method that allows, on the one hand, the identification of characteristic microscopic changes in the digital template during the first print, and on the other hand, the evaluation of the changes undergone by the copy relative to the original as an exclusion criterion for proof of originality. Furthermore, a part of the invention is that the camera on a common smartphone with dedicated software is sufficient to identify the desired microscopic details on the printed image. The method of the invention can also be applied to color prints. The proposed method is particularly aimed at protecting original products from counterfeiting.

[0019] The printing method and authentication method for a printout of a digital image according to the present invention include: printing an authentication mark by applying amplitude-modulated raster printing in a probe area onto an object, wherein the printed surface of the probe area is composed of asymmetrical raster dots, wherein at least two non-parallel framing edges in at least one framing area are printed to determine the position, boundaries, and orientation of the probe area; and a method for authenticating such a printout, the method including providing an image recording device having a microprocessor for implementing the authentication procedure, providing a print image derived from print data predetermined from a predetermined number of raster dots of the printed object in the probe area, and providing a computer program for comparing the print image predetermined from the raster dot data; wherein the method includes: recording an image of the printed object; identifying at least two framing edges to determine the probe area from the image in a raster dot-precise manner; comparing the recorded print image of the probe area with the derived print image; and determining, based on the comparison, whether an original printout exists on the printed object.

[0020] Advantageously, each viewfinder edge is composed of rows of raster points arranged side-by-side along a predetermined segment of the printed image, wherein the differences between the raster points in the side-by-side rows are selected from the following group: symmetrical and asymmetrical raster points, predetermined different raster angles of the raster points, and AM and FM modulation of the raster points, wherein said differences can be preset differently or identically from the group used for each viewfinder edge in a manner unrelated to each other. In other words, the viewfinder edge can be defined by the differences between symmetrical and asymmetrical raster points (such as in...). Figure 12 As shown in the diagram, one framing edge is determined by the difference between AM and FM modulation of the raster dots in the rows on one side and the other side of the framing edge. As long as both framing edges are associated with the same framing area, the compatibility of the areas on the framing side must be determined.

[0021] The distinction between the raster dots in rows side-by-side at the viewfinder edge can also include different AM modulations of the raster dots on either side of the viewfinder edge. The distinction in AM modulation can be achieved, in particular, in the amplitude or frequency of two AM modulations, or, if necessary, at least two AM modulations of at least one color.

[0022] Therefore, the framing area defined by the framing edge can have an asymmetrical raster dot shape, wherein raster dots existing beyond the framing area on the other side of the framing edge respectively form areas with symmetrical raster dot shapes from the remaining printed image.

[0023] Alternatively, the framing area defined by the framing edge can have a symmetrical raster dot shape, wherein raster dots existing beyond the framing area on the other side of the framing edge form areas with asymmetrical raster dot shapes from the remaining printed image or from the detection area.

[0024] Alternatively, the framing area determined by the framing edge can have a symmetrical grating dot shape with a first grating angle, wherein the framing area extends beyond the framing edge to adjacent areas in the remaining printed image or detection area with a second grating angle (where the first and second grating angles are different from each other).

[0025] A predetermined number of asymmetric grating dots in the detection zone can be arranged in a matrix consisting of at least two rows and two columns; the example shown is based on a length of at least three rows and 10 or more grating dots, but smaller numbers are feasible in principle. In other words, a predetermined number of grating dots in the detection zone can be divided into regions with asymmetric and symmetric grating dot structures, wherein said regions are arranged in a matrix consisting of at least two rows and two columns.

[0026] In multicolor printing, asymmetrical raster dots are set in one of the two ink layers that are best visible and evaluable for the final printing.

[0027] In the case of color printing, the framing edge can also be set by determining the shape of the raster dots and / or the raster angle of the same or different ink layers.

[0028] Advantageously, the asymmetric raster points to be evaluated have grayscale values ​​between 25% and 75%. The same applies to symmetric raster points, although smaller and higher values ​​up to 100% are also possible there.

[0029] At least two framing edges can meet at the corner of the framing area, making the framing area directly identifiable, or the framing edges of one or more framing areas are located at the edge of the printed image or in at least a pair of intersecting framing strips.

[0030] In the method of printing authentication marks by applying amplitude-modulated raster printing in the detection area, a comparison basis (matching template) is generated based on the print data consisting of a set of data of the print substrate, print ink, and print guide.

[0031] Advantageously, the comparison basis is trained using original printouts and strip proofs, wherein optionally, the recorded image of the printed object undergoes image conversion in the format of the comparison basis for direct comparison via a graphical algorithm in the authentication method.

[0032] Therefore, preferably, the recording of the image of the printed object in the authentication method can include recording multiple images using different camera parameters consisting of a group including focus changes and exposure time changes, in order to generate an image stack, the data of which is reshaped into an aligned image stack for subsequent conversion into a more basic format. This allows for increased resolution, and also makes it easier to use simpler cameras from mobile communication devices.

[0033] The predetermined matrix containing digital information includes the distribution of viewing areas and (multiple) detection areas.

[0034] The detection zone can be checked based on the recorder elements that make up the grating points contained therein by means of a comparison basis, and a threshold is used to compare the consistency of the detected recorder elements with the corresponding recorder elements of the comparison basis.

[0035] Advantageously, multiple separate detection zones are provided, and the total threshold obtained through all detection zones or the individual threshold of a single detection zone is then used as the basis for decision-making.

[0036] Starting with a digital template of raster dots in a pre-printed image, a soft-edge step is introduced, in which a soft-edge model is generated from the digital template based on data comprising a print substrate, print ink, and print guides. This model can optionally be trained in a subsequent training step using an original print or strip proof of the printed model used for training, to create a matching template for image analysis of selected regions of the printed image to be inspected. The matching of the matching template and the dataset of the image to be authenticated provides a conclusion of "original" or "copy" after applying a quality matrix.

[0037] A graph algorithm can be used to transform the printout to be inspected into a dataset with the same architecture as the matching template, wherein optionally, the mathematical form of the raster pattern is equivalent to a dense network of nodes and edges aligned with the raster points of the printed image.

[0038] Before applying the graphics algorithm, the print to be inspected is detected by generating an image sequence using different camera parameters consisting of a group of focus changes, especially in unequal step sizes, exposure time changes, and camera position changes. The resulting image stack is aligned in an alignment step to obtain an alignment vector field, wherein other parameters varying between the images in the aforementioned group are subsequently obtained to obtain a result, which is processed by the graphics algorithm (58). Attached Figure Description

[0039] Preferred embodiments of the invention are described below with reference to the accompanying drawings, which are for illustrative purposes only and should not be construed as limiting. The drawings show:

[0040] Figure 1A A schematic diagram of a raster dot shape for use within the scope of a printing method according to an embodiment of the present invention and a print thereof as an original or a copy are shown.

[0041] Figure 1B A schematic diagram of another raster dot shape for use within the scope of a printing method according to an embodiment of the present invention and a printout thereof as an original or a copy are shown.

[0042] Figure 2A A schematic digital template showing a matrix of 11×10 grating units with irregularly shaped grating dots of size 6×6 recorder elements;

[0043] Figure 2B A schematic digital template showing a matrix of 48×24 grating units with grating dots of size 4×4 recorder elements, wherein eight detection zones are provided;

[0044] Figure 2C An image showing a region consisting of at least one irregularly shaped raster dot is displayed;

[0045] Figure 2D The image shown is a photograph taken as a digital image template (craft).

[0046] Figure 2E The image shown is a photograph taken as the original print.

[0047] Figure 2F The image shown is a scanned copy of the original printout, i.e., a photocopy.

[0048] Figure 3A A schematic diagram showing an image with "content-free representation" for each region;

[0049] Figure 3B A schematic diagram showing the images of the areas with their respective defining settings;

[0050] Figure 3C A schematic diagram showing the images of the areas with their respective defining settings;

[0051] Figure 3D This shows a region with grating dots of different constructions and another combination of two braided strips;

[0052] Figure 3E This shows a region with grating dots of different structures and another combination of three-by-two braided strips;

[0053] Figure 3F This shows another combination of regions with different raster dots and surrounding region edges;

[0054] Figure 4A Showing partial images of two images with regular raster dots;

[0055] Figure 4B Showing partial images of two images with irregular raster dots;

[0056] Figure 4C This shows a captured image with a partial view of it.

[0057] Figure 5A This shows a captured image with a partial view of it.

[0058] Figure 6A Showing the digitally preset raster dots and their original print;

[0059] Figure 6B The original printout of digitally preset raster dots, its scan, and a reprintout of a copy derived from the scan are shown.

[0060] Figure 7 An illustration showing the imaging of grating dots by a camera, especially a smartphone camera;

[0061] Figure 8A A flowchart illustrating a comparison method based on digital print templates and contrasting prints;

[0062] Figure 8B This demonstrates an auxiliary method for improving camera resolution;

[0063] Figure 9 This diagram illustrates a comparison of the grating unit size of the grating dots relative to the resolution of a 12MP smartphone camera and the application of methods to improve resolution.

[0064] Figure 10 Three sets of 2×3 grating dots are shown, each grating dot having a different alternating order;

[0065] Figure 11 The image recognition process is illustrated in three planes; and

[0066] Figure 12 A schematic diagram showing an image with various zones and framing edges is provided. Detailed Implementation

[0067] Figure 1A or Figure 1BSchematic diagrams of raster dot shapes 1 or 4 for use within the scope of a printing method according to an embodiment of the present invention, and their prints as originals or copies, are shown respectively. Raster dot shapes 1 and 4 may also be referred to as digital templates. The raster dots must have a sufficient size, for example, 8×8 or 12×12. Hereinafter, in conjunction with the description of the invention... Figure 2A The size is based on 6×6. Figure 2A A schematic digital template showing a matrix 7 of 11×10 grating units with irregularly shaped grating dots 8 having a size of 6×6 recorder elements.

[0068] The selection criteria for raster point shapes can be arbitrary, for example, based on specific or uncommon raster point definitions in the raster image processor (RIP machine) used during rasterization in pre-printing. In such cases, the shape of the raster point is forcibly associated with a specific hue value. However, the raster point shape can also be freely defined and only follow the rule that one raster point is placed at a hue value for a specific number of printable recorder elements (also the smallest printable portion of the raster element = raster point), otherwise, the shape of the raster point can be arbitrary. Arbitrarily shaped raster points can be generated in typesetting, where the RIP is set up such that predefined raster points are used invariably for the print template. The subject of this invention is not the creation of raster points themselves, but rather the suggestion of how to distinguish original prints from copies of those prints in a simple manner by means of their unusual geometry. Decisive to embodiments of the invention is how specially designed raster points 8 can contribute to the manner and method of tamper-proofing of images. Recommendations for constructing raster dots themselves are known, such as US 8'456'699B2 (Raster Dots (Print Dots or Clustered Dots) based on the growth of selected raster elements (pixels)).

[0069] In this invention, verification of the original should be performed using a simple mechanism, preferably a smartphone. Therefore, this invention can be described as describing an anti-tamper indicator integrated into an image, recognizable by a smartphone with a corresponding application. Another option is a combination of the anti-tamper indicator and an embedded message, which is another advantage of this invention.

[0070] The tamper-proof indicator is essentially composed of a set 7 of grating dots 8, preferably arbitrarily shaped. This set 7, or matrix, is introduced into a base image at predetermined positions within an area of ​​predetermined size. The base image, as described below... Figure 4A Image 26 or Figure 4C Image 33 or Figure 5AImage 34 in the image is a printed side provided for an observer, such as on packaging. The unprinted side is typically outside the basic image. Conversely, Figure 12 The base image 210 has edges 213. Multiple regions can be integrated into the base image at different locations. All raster points in the base image outside the regions can have shapes common to amplitude modulation (AM) raster printing, such as circles or ellipses, but are not necessarily so. The method described herein can also be applied to a mixture of frequency modulation (FM) and amplitude modulation (AM) rasterization. However, the indication of the originality of the print can naturally only be performed by means of image elements (i.e., such set 7) subjected to AM rasterization according to the invention. The indication area is not necessarily required to be composed of raster points of obviously arbitrary shape, but can also be composed of raster points whose geometry is significantly different from the raster point structure of the base image. For example, it is conceivable that the environment of the marking area is composed of circular raster points, and the marking area or detection area is composed of raster points of obviously elliptical shape. The term "obviously arbitrary" shaped raster points implies that raster points of different irregular shapes can be designed in different ways. On the one hand, as long as the number of raster elements being printed produces the desired hue or grayscale value, it is conceivable that the composition of raster points, consisting of the raster elements being printed or the recorder elements, can be calculated purely randomly. On the other hand, irregularly shaped raster points can also be generated in a systematic manner; for example, it is conceivable that unorthodox parameterization of thresholds in raster image processing, as proposed in EP-A-3 686027, can be used.

[0071] The indication of the originality of the print can be implemented as described above, especially by means of only image elements subjected to AM rasterization according to the invention (i.e., such a set 7 in one or more zones). The raster dots in the base image outside said zones may have shapes common to amplitude modulated raster printing (AM raster printing), such as circular or elliptical, but not necessarily. This describes a method in which one (or each) framing edge has two zones with image elements that are amplitude modulated (AM) rasterized differently.

[0072] Figure 2B A schematic digital template is shown, representing a printed surface or matrix 9 of 48×24 grating units with grating dots of 4×4 size recorder elements, comprising eight special sub-surfaces. Each grating unit includes twelve grating elements for printing, corresponding to 75% color coverage. Within the entire surface exist eight surface elements, sets, or special sub-surfaces composed of 3×3 grating units with irregularly / asymmetrically shaped grating elements, where the color coverage on the sub-surface corresponds to the color coverage on the total surface. In other words, each special sub-surface corresponds to... Figure 2A The set of grating points 7. Figure 2BA uniform surface 9 with a grayscale value of 75% is generated, which contains a total of eight sub-surfaces with raster dots of an asymmetrical design. In this example, all eight sub-surfaces contain the same pattern; that is, the raster units are designed identically in all eight surface elements. This is not mandatory; the individual marker areas can be designed differently. It is also possible for some marker areas to be designed identically while others follow different patterns. The only rule for the construction of each particular sub-surface is that the halftone of the image template is not changed. The particular sub-surface has a field of view 19 or 20 (in Figure 3A The central area is divided into orientation and synchronization markers) or functions as a detection area 21. If the framing edges 211, 212 are implemented in the detection area, the special sub-face can also combine two functions, namely the framing area 19 / 20 and the detection area 21. If multiple such faces exist, the separation of functions in different regions of the basic image can accelerate the detection of the special sub-face through images 26, 33, 34 or 210 recorded by the camera, because the first such face can then be identified more quickly. Figure 2B Examples of framing edges 211 and 212 are drawn in the image.

[0073] Advantageously, the special sub-facet differs from the surrounding basic image 210 in its grating structure. However, it is also possible that the special sub-facet detection region occupies only a portion of the facet printed with asymmetrically constructed grating dots, and that only one or more detection regions 19 or 20 have, for example, symmetrical grating dots. (Brief reference) Figure 3A It is determined that the adjacent base image 210 can have exactly the rasterization of the detection area 21. Essentially, there are at least two non-parallel framing edges 211 and 212, which are not necessarily associated with the same framing area 19 or 20. The non-parallel framing edges 211 and 212 are characterized in that the raster structure within the framing area 19 or 20 is different from the raster structure outside the framing area 19 or 20, i.e., in the adjacent base image 210, wherein it is possible that the detection area 21 is adjacent to one or more framing areas 19, 20. Framing edges 211 and 212 can be sides of framing areas 19, 20, 190, and can also be associated with different framing areas 19, 20, 190. Multiple framing edges 211 and 212 can also exist, as in... Figure 3A The image is shown by referring to the edges of two zones 20 and using dashed lines on the two edges of one zone 19 and the other zone 20. Here, it is advantageous to determine the length of at least one framing edge 19, 20, 190. From this, it is concluded that the size of the framing area is known from the analysis of the different edges. At least one length of the framing edge should be known. In this regard, Figure 3AThe dashed lines in (and other diagrams) are illustrative only for the area covered by the framing point. The dashed lines highlighted here only indicate orientation, and the framing edge is simply a segment of road (a segment in a mathematical sense, which is an undirected vector of length and position relative to the framing point in 2D space) with the framing point as its edge.

[0074] The condition that the framing edges 211 and 212 are not parallel to each other can also be referred to as intersecting framing edges. The intersection point can exist, for example, as a corner point of the framing area 19 in the evaluation, although image evaluation does not require the intersection point to be used as a corner point of the framing area. In the case of non-parallel lines, the intersection point can be outside the image / print, because what is crucial here is the length of the framing edge segment, especially the length beside the orientation, rather than the record of the intersection point itself. Nevertheless, the orthogonality of the framing edges 211 and 212 is preferred, as this simplifies the determination of the position, boundaries, and orientation of the detection area 21. Besides determining the detection area 21 precisely from the direct pixels of the framing edges 211 and 212, one or more framing areas can first be determined so that the detection area 21 can be subsequently determined based on these. In extreme cases, only two edges of the framing edges exist as the detection area 21 of the framing edge.

[0075] Figure 2C Finally, an image 11 is shown, showing at least one region 12 consisting of irregularly shaped grating dots. Region 12 here corresponds to the detection area. If necessary, it may also be a field of view 19 or 20.

[0076] Figure 2D An example illustration of the portrait image in the original or digital image template 13, the original 14, and the scanned copy 15 of the original print 14 illustrates the alteration of the digital image template (original) via the original print towards the scanned print, i.e., the copy, wherein the pre-formed loss is clearly recorded again in the magnified local area at 12×8 raster dots of the right eye 16, 17, or 18 of the portrait shown.

[0077] Figure 3AA schematic diagram of an image with “content-free representation” having zones 19, 20, and 21 is now shown. The functions of the marking zones can be of different natures. In particular, the framing markers or starting markers 19, which characterize the position, boundaries, and orientation of the basic image, should be distinguished from the markers used for image correction. With the aid of such markers (alignment markers) 20, the stretching, compression, and internal distortion of the image can be modified computationally, thereby enabling robust optical analysis of the image at the microscopic level. The markers should be understood as auxiliary zones whose purpose is to present the image in a manner suitable for image analysis to some extent. Due to their microstructure, the markers are not visible to the naked eye, but can be identified by means of optical aids. These zones are generally referred to herein as framing zones 19 / 20. The framing zones need not be located at the edges of the image. The object of the invention is precisely to generate / print the framing zones 19 / 20 by means of a printing method, which can be identified by the method, but are not visible to the natural observer as framing zones.

[0078] The identification area 21 or probe area, including a genuine tamper-evident indicator, for checking the originality of the printout is located at a selected location in the image and analyzed in a point-precise manner. The position of the identification area 21 or tamper-evident indicator can be fixedly preset, or it can also be present in the viewfinder marker in an coded manner. Areas 19, 20, and 21 can also be adjacent to each other. The component referred to as the surrounding image 210 can have the same raster print as the probe area 21, but it is not required to do so. Basically, there are at least two viewfinder edges 211 and 212 that are not oriented parallel to each other, and the viewfinder is the edge of one or more viewfinders.

[0079] Here, the viewfinder edges 211 and 212 not only mean the lines drawn here as auxiliary lines, but also mean rows of different grating points existing side by side along the path, wherein the difference between the grating points in the rows of side by side is selected from the following group: symmetrical grating points and asymmetrical grating points, predetermined different grating angles, AM modulation and FM modulation.

[0080] When necessary, the functions of the viewfinder markers, alignment markers, and tamper-proof indicators can be combined. For example, Figure 3BThis feasibility involves an image being covered in a checkerboard pattern, not only horizontally but also vertically, by alternating regions 23 with asymmetrical and symmetrical raster point structures. Based on the checkerboard alternation of regions with regularly and irregularly shaped raster points, an image can be constructed from a large number of regions of different raster point shapes covering most or all of the image. For example, assuming two different raster point shapes, a region with a standard circular raster point shape 23 can be associated with the value "0," while a region with an asymmetrically constructed raster point shape receives the value "1." Essentially in this embodiment, the size of the regions is normalized to a value that, in multiples, describes the size of all raster regions; as a result, bit codes 24 are generated from an assignment of sub-faces with standard dimensions, for example, from 100×100 raster faces, where sub-faces correspond to 1 and sub-faces 23 correspond to 0. Figure 3C Another example implementation with adjacent bit sequences 25 is shown. According to... Figure 3B or Figure 3C In the example, the parity of the two region types The number of normalized face elements, shown as squares, is the same for both raster point shapes in both images (35 face elements for each raster point shape). Other parity values ​​are also conceivable, for example, 40 face elements with asymmetrical raster points and 30 face elements with symmetrical raster points. In addition to the special configurations of the raster point shapes themselves that can be used for tamper-proof inspection, the distribution of regions thus provides the feasibility of hidden coding, where parity is an additional characteristic value that complements the information behind the hidden coding. Another option for this embodiment is based on the composition of a basic image consisting of three or more regions of different raster point shapes, such as circles, crosses, and irregular shapes, so that high information density can be achieved through region coding in the aforementioned manner.

[0081] According to Figure 3B In the design scheme, the viewing area 190 can be, for example, an area with an asymmetrical raster dot shape, and the area 23 with a symmetrical raster dot shape is adjacent to the area with the asymmetrical raster dot shape, which in this case is adjacent to the viewing edges 211 and 212. According to... Figure 3C In the design scheme, another viewing area 190 is provided, for example, an area 23 with a symmetrical raster dot shape, and an area with an asymmetrical raster dot shape is adjacent to the area with the symmetrical raster dot shape, which is exactly adjacent to the viewing edges 211 and 212. Important for the recognition method is edge recognition through the altered raster dot shape, which is invisible in the image.

[0082] Importantly, grayscale values ​​in the range of 20% to 80%, or, in the case of color printing, the corresponding halftone values ​​of the printing ink, particularly 25% to 75%, are used to identify the differences between symmetrical and asymmetrical points in the raster dots at the recorder element level for image evaluation. At higher or lower values, these framing edges 211, 212 gradually become typical edges, which can also be recognized by the naked eye because the subsequent transition from asymmetrical to symmetrical raster dot elements is no longer identifiable, and the image components include the edges. However, framing edges also exist if, for example, asymmetrical raster dots and / or specific raster angles are typically arranged in multiple rows side-by-side on one side of the framing edge, while on the other side, if necessary, multiple rows of a single color are printed with grayscale values ​​of 80% to 100%. This is because a symmetrical distribution of raster dots with 100% grayscale values ​​corresponds to a printed edge.

[0083] Figure 3D , Figure 3E and Figure 3F The drawing illustrates other embodiments of combinations of raster point regions with different configurations, where the same reference numerals 210 and 211 are used for regions having specific raster point shapes. This also applies to other image components 210 and viewfinder edges 211 and 212. As an example, in Figure 3D In the image, the upper left corner is defined as a framing area 190 with symmetrical raster dots, where two framing edges 211 and 212 abut against two strips 23 with asymmetrical raster dots. The area 21 with asymmetrical raster dots in the lower horizontal strip is designated as the detection area. Other image components 210 are other areas of the image. However, other framing edges (not shown here) can also be provided to allow for a double-intersecting structure of strips with asymmetrical raster dots for faster image detection. Figure 3E In the image, the upper portion of the second strip with asymmetrical raster dots, having corresponding framing edges 211 and 212, is the framing area 190, and the middle portion between the two horizontal strips from the first vertical strip on the left is the detection area 21. For those skilled in the art, other framing areas and detection areas can be easily inserted. Figure 3D and Figure 3E In the implementation scheme, strips 211 and 212 do not necessarily have to be perpendicular to each other, but framing edges 211 and 212 can be more easily determined in a vertical configuration. The term framing edge refers to a group of at least one row, preferably multiple rows of grating dots on both sides of the virtual framing edge, wherein the "row" is not parallel to the framing edge on at least one side, and if necessary on both sides, at different grating angles, but is angled to the framing edge.

[0084] exist Figure 2BThe image shows eight image regions, each with a 3×3 raster unit. In extreme cases, the identifier region 21 can also be composed of a single raster point. For example, in Figure 10 The image shows three sets of 2×3 grating dots with different configurations (regular and irregular), each with a different alternation order. Regularly shaped grating dots 73 alternate directly with grating dots 74 with unique shapes. According to... Figure 10 The original image provides a known pattern of regular raster dots throughout the file, which can be digitally re-identified in image analysis and allows for more precise analysis of irregularly shaped raster dots.

[0085] Figure 4A Image 26 is shown with two magnified image portions 27 and 28. Figure 4A The imaging has a relatively low resolution of 40 lines / cm. Higher resolutions, such as, for example, 100 lines / cm, can also be readily achieved for the method according to the invention. In offset printing, a resolution of 80 lines / cm is a good value for the captured image, while 100 lines or higher represents excellent quality. Figure 4A In this example, a relatively low resolution was used to better display the grating structure. Figure 4A Image 26, constructed from circular raster dots, is shown in magnified details 27 and 28. In contrast, Figure 4B Showing with Figure 4A The same image, but with different image constructions 29 consisting of asymmetrical raster dots, as can be seen in parts 30 and 31. Figure 4C Image 33 is composed of circular raster dots, with a small locality 32 in the lower left corner 32, which is constructed of asymmetrical or irregular raster dots. The magnified locality 32 in a rasterized image, generally composed primarily of circular raster dots but in local regions almost entirely of irregularly shaped raster dots, here has a narrow edge of a row of circular raster dots in the local magnification, indicating a (different) rasterization of the overall image. For example, the size and location of this locality can be used as a starting point for image analysis. For this purpose, Figure 4C as well as Figure 4B Then, at least one local area is designated as detection region 21, which is plotted within the grassy area. Region 21, like region 190, is composed of asymmetrically constructed raster dots. Figure 4C In this context, the detection area can also be the only viewing area 190. Therefore, the area 190 is both the viewing area and the detection area.

[0086] In other words, Figure 4CPart 32 shows a field of view 190 with two mutually perpendicular framing edges 211 and 212, wherein the field of view 190 is composed of asymmetrically formed raster dots, which in turn adjoin the remaining image 210 with their edges 211 and 212, the remaining image being composed of symmetrically formed raster dots in at least three rows shown next to the field of view 190.

[0087] Figures 4A to 4C All images, and in their respective localities, have a raster angle of 0°. It is conceivable that raster points in the base image and localities are represented by different raster angles, for example, a 0° raster angle for symmetrical raster points and a 60° raster angle for asymmetrical raster points. It is also conceivable that the entire image, excluding the detection area, is represented by symmetrical raster point shapes, where the base image and localities of detection areas 19, 20, and 190 are distinguished only by different raster angles. The key distinction is defined by the raster system in the fields 19, 20, and 190 being different from the raster system in the base image 210. The difference in the raster angle system is sufficient to distinguish the fields 211 and 212 when the raster point shapes are the same, but the difference is more noticeable to the naked eye. If different raster angles are used for distinction between the base image and the encoded image portion, then a particular evaluation is appropriate because image elements with different raster angles can be visibly highlighted from the base image. Empirically, this is the case in low-resolution grayscale images, where color effects can also be additionally altered in color images because the alterations are always coordinated with the raster angle, and visible discontinuities appear when the raster angle changes. However, furthermore, the apparent quality is related to the subject and the selected image locale.

[0088] Figure 5A An initial color image 34 is shown, in which a yellow object 134 is embedded in a substantially blue background 135. Here, "background" means that the observer sees the object 134 in front of the background. However, in printing technology, the background 135 is dominated by raster point printing elements 136 involved in the last, i.e., "foreground" printing task in time. Thus, here is a grayscale illustration of a color image consisting of cyan and magenta rasteres in the background, and additionally consisting of yellow rasteres in the area of ​​the subject (pigeon). This disclosure relates to a color illustration in which cyan rasteres form the uppermost layer, and the tree-like shape of the cyan raster points 136 is well identifiable in magnified local areas of the image.

[0089] The advantage of this method in altering the color space is the improved recognizability of the image recording system, especially at low resolutions. This is combined with the principle described above of distinguishing the raster point shapes of the basic image from the raster point shapes of specific other image portions composed of raster points of other geometric shapes. Figure 5A Detailed Explanation. Image 34 shown consists of magenta and cyan in addition to the non-realistic bird subject, with cyan being the upper ink layer. The ink layer below it is composed of magenta line raster 137. The image of the bird also additionally includes yellow as the lowest ink layer, whose raster dots are not so well suited for image analysis. It is confirmed, especially in part 35, that the uppermost cyan layer has raster dots with clearly visible, independent geometry at the microscopic level (here essentially raster dots that appear to be irregularly shaped). Figure 5B A further magnification of portion 35—denoted by reference numeral 35a on the left and by reference numeral 35b on the right as part of 35a—clearly shows the independent shapes of the cyan raster dots, some of which are indicated by reference numeral 136. For clarity, the outlines of the cyan raster dots 36 are shown in separate portions 35b next to the portion representing the grayscale values ​​of 35a. In other words, the raster dots of at least one color in the plurality of ink layers have independent geometries.

[0090] The scanned printout has further deformation of the raster dots on the top (= the last printed ink layer), thus being recognized as a copy by means of a digital image recording device combined with specialized software. The original printout itself is created from a digital image template and develops into a printout in a calculable or predetermined manner and method due to the influence of printing method, color, and media characteristics during the printing process; the printout is similar to a fingerprint of the original.

[0091] In this invention, the printing steps that result in the "original" and "copy" can be described in principle as follows, in which, exemplarily, in... Figure 6A and Figure 6B As shown, the preset clear outline image 37, the raster dots are blurred into print dot elements 38 during printing, and after scanning the print dot elements 38, they are converted into a new digital raster image 39, which is further blurred in the resulting copy 40 after reprinting. Advantageously, in the first step for identifying the original print, the scale of the raster dot outline resolution of the digital template can be predicted based on a mathematical model, thereby enabling image analysis and comparison to be performed using a smartphone.

[0092] A digital template should be understood as raster data used for plate making, such as a file used in offset printing for a laser imagesetter. The corresponding file contains all the data regarding the construction of all raster dots for the color separation of the image to be printed. Ideally, each raster dot consists of a group of square pixels, each of which individually derives the raster dot. The transfer of printing ink to the printing medium, such as coated paperboard, is a physical process, in which the rheological properties of the ink used, the characteristics of the printing medium, and various influencing factors such as method control, such as the amount of ink applied, further cause deformation of the raster dots.

[0093] The deformation of raster dots under given printing conditions can be described using a point spread function (PSF, also known as a blur kernel). Known PSFs are based, for example, on a two-dimensional Gaussian distribution (Gaussian smoothing) or mean filtering (mean filtering) formed by the average of neighboring pixels. The PSF describes the printed image as a function of all major printing parameters, especially the ink flow and drying characteristics, the ink absorption of the medium, and process control. Advantageously, a mathematical model 48 for raster dot softening is trained 49 for preset printing conditions. Preset conditions include, for example, the type of paperboard used, the ink, and presets for guiding the printer, such as ink coating. It is particularly advantageous to train the mathematical model for each subject matter, such as an image subject on the original packaging of a specific brand of product. This trained model 50 for raster dot broadening on the original packaging produced by a printing process certified by the model is advantageously used as a standard for verifying the originality of the packaging, which can be performed anytime and anywhere using a suitable image detection device (smartphone) and dedicated software.

[0094] Figure 6A This example illustrates how raster dots are widened and deformed during the printing process when manufacturing the original print.

[0095] For certification, requirements are placed on the image recording system in terms of hardware and recording methods, requiring it to achieve resolution down to the size of the grating element, i.e., the smallest printed portion of the grating dot. An image printed using offset printing is considered a high-quality print if the grating has a frequency of 80 lines per centimeter or less. 80 lines / cm corresponds to a size of 15.6 μm for the grating element. It can be shown that the detection of grating elements of this size cannot be achieved in a single shot using a traditional smartphone camera. Figure 7 The diagram illustrates the imaging relationship between the camera and the image to be recorded. For example, a 1 / 1.8-inch sensor with an aspect ratio of 4:3 can achieve a resolution of 9310 × 7000 pixels, or 65 megapixels. For simplicity, here we will refer to the sensor size... Figure 7 Only the output is shown. This is a value achievable by high-end smartphones according to the latest available technology. If we further assume that the smartphone camera must have a certain distance 43 from the printing medium 41 to be inspected in order to produce a sharp image of the image region 42 to be analyzed, for example, 130mm × 98mm, then the resolution results in a pixel pitch of approximately 14μm. This pixel pitch enables a size of 0.112mm for the grating unit, provided that the grating unit is composed of a matrix of 8×8 grating elements. This size of grating unit allows a grating frequency of 90 lines / cm, which is sufficient for high-quality offset printing or high-resolution flexographic printing. This is a preferred method for packaging printing. However, it is impossible to record a grating frequency of 90 lines / cm in terms of imaging technology using a sensor with the same pixel frequency. According to the Nyquist-Shannon theorem, the sampling rate must correspond to at least twice the frame rate. This condition, according to the signal theory in the example above, results in a specification corresponding to 18'620 × 14'000 pixels at 260 megapixels. This is a value that is unattainable by current common cameras in smartphone formats. A resolution of approximately 100 megapixels remains a limit for commercial camera systems. In commercially available mid-range smartphones primarily used by consumers, 12 megapixels is common. This excludes the use of simple smartphones for optical analysis of raster dot shapes using classic image recording. The resolution limits of mobile phone cameras are not suitable for dedicated camera systems with high-resolution full-frame and medium-format sensors combined with macro or replica lenses with an image ratio of 1:1 or higher. These systems at least partially possess a resolution of 60 to 100 megapixels, resulting in a pixel pitch of less than 4 μm at an image ratio of 1:1.

[0096] As an image recording device, the smartphone is used to perform preferred image analysis according to the invention on rasterized images with the help of a typical 12-megapixel smartphone camera, however, supported by super-resolution and / or mathematical deconvolution methods. This is also used in astronomical applications and microscopic recording. Super-resolution has long been considered prior art (see, for example, Borman et al., Super-Resolution from Image Sequences, Department of Electrical Engineering, University of...). (NotreDame, 1998). For super-resolution-based image enhancement, software is available for consumer and less specialized applications, such as Chasy Draw IES or Topaz Gigapixel AI.

[0097] In super-resolution and deconvolution methods, see, for example, "Pragmatic Introduction to Signalprocessing," Tom O'Haver, Department of Chemistry and Biochemistry, The University of Maryland at College Park; available at https: / / terpconnect.umd.edu / ~toh / spectrum / TOC.html. Essentially, multiple images are used, recorded under substantially similar conditions, but differing only slightly or moderately under one or more of these conditions. From these differences, information about fine resolution can be derived. The goal of this method can be either high-resolution images or the direct measurement of high-precision features on images with low resolution. Scene content, focus, exposure, and the position and motion of the smartphone affect the results of this method.

[0098] As in Figure 9 As shown, the size of the grating unit 66 is derived from the grating frequency, which, for example, results in a size of 14 micrometers at a frequency of 90 lines / cm in the case of an 8×8 grating element grating unit. The image recording chip of a smartphone 67 with a resolution of 65MP has a resolution of approximately 14 micrometers, which is insufficient for scanning or sampling grating element sizes of the same size. Sampling requires a resolution corresponding to a pixel pitch of 7μm, as indicated by the square 68.

[0099] Super-resolution methods typically achieve a 2-4x resolution improvement, resulting in a 9-micron sampling rate for raster elements at 69 in the case of a 12-megapixel image. Deconvolution methods correspond to a similar approach, however, based on very blurry images recorded from smaller distances. The combined use of super-resolution and deconvolution can result in an 8x increase in sampling frequency compared to normal recordings at typical minimum distances, achieving a resolution of approximately 4 microns70 for measuring point characteristics. Therefore, depending on the camera model used or to be used, comparisons can be performed directly after applying the super-resolution method and / or after applying the deconvolution method.

[0100] Based on this approach, what is essential in a range of cameras, especially smartphones, is the use of image authentication that results in higher resolution and quality improvements. This authentication can be achieved through a series of separate recordings of short video sequences or images, for example, by a smartphone with a 12-megapixel camera, using a suitable super-resolution algorithm. Common smartphone sensors combined with super-resolution algorithms are sufficient for this. Alternatively or additionally, deconvolution methods, such as those integrated into Matlab and Octave, can also be used.

[0101] Each of the methods described starts by detecting multiple images using some fixed parameters, such as resolution and light gain, some of which are unaffected or unknown. First, the smartphone's position is preset by hand guidance, causing movement at a speed of a few mm / s along the X, Y, or Z directions. This translates to a 60 μm offset or a 1-3 pixel / s movement in the image plane for a movement of 1-2 mm / s. Ambient light also has an effect, especially some types of neon lights. Therefore, due to small offsets and lighting conditions, the resulting images differ slightly. Furthermore, camera shake can also occur during shutter speed, leading to blurring.

[0102] Figure 8A A flowchart illustrating a method for identifying a copy without considering auxiliary methods for improving resolution (i.e., especially the aforementioned super-resolution and / or deconvolution methods) is presented, starting with an original work (i.e., a digital template) 46 generated in a pre-print. A soft-edge model 48 is generated from the original work 46, parameterized with data from the printing substrate (cardboard, paper, etc.), printing ink, printing guides, etc., and optionally trained with the original print or a strip proof to obtain an optimized version of the initial model 48. The trained model 50 provides a better comparative basis (template matching) for more robust image analysis of selected regions of the printed image to be examined, compared to the untrained model 48. The template matching 53 and the dataset of images to be authenticated, after applying a quality matrix 54, lead to the conclusion of "original" or "copy".

[0103] A matching template 52 that approximates a canonical version of the original printout can be formally described by nodes and edges according to geometric graph theory, as indicated by reference numerals 51 and 52. However, other methods are also feasible for characterizing the template. For example, the content fingerprint method according to EP 2 717 510 B1 is also suitable.

[0104] In the case of graph theory methods, the printouts to be inspected are transformed into a dataset with the same architecture as template 52 using graph algorithms. These printouts may be photocopies, digital templates, or originals. In extreme cases, the mathematical form of the raster pattern is equivalent to a dense network of nodes aligned with the raster points of the printed image.

[0105] Considering auxiliary methods for improving camera resolution, it is necessary to... Figure 8B A single recorded sequence or video stream.

[0106] The inspection of the printed parts is performed using different camera parameters 60. Key differences in the raster points are revealed by analyzing the blurred deconvolution of the video stream (analyzed as a single image) by changing the focus with non-equidistant step sizes. Variations in exposure time are also used to reveal microprinting features, thereby balancing the light differences from a 50Hz light source. As a result, an image stack 61 is obtained. The method calculates alignment from multiple individual images 62 to obtain an alignment vector field that forms the basis for image synthesis with high resolution. Parameters varying between images, such as lighting conditions, are estimated in a similar manner. The aligned images are then processed 64 to obtain a result 65. Here, a mathematical representation for the high-resolution image is generated, which can be compared with a matching template 52.

[0107] The process of aligning individual images begins with the accurate superposition of reasonable registration of each image, a simple step even in cases of image blur. In the next step, information about the precise location of the procedural raster points is fed into the process. Here, the location of the procedural raster points at different time points must be known. As regular (procedural) and irregular raster point configurations alternate, smaller portions of the image can be attempted to align with pixel offsets, one along the x-axis and the other along the y-axis, until a correct procedural raster point alignment is found. The alternating pattern limits the number of processes that must be performed. Therefore, regularly shaped raster points are advantageous for the deconvolution process.

[0108] The process of image recognition is in Figure 11 The diagram shows that processing is applied to an image 75 aligned at a macroscopic level to progressively obtain an intermediate version 76 and finally a high-resolution version 77. The quality of the method is measured based on its consistency with a known reference pattern of raster points that are regularly shaped at the highest resolution. This measurement is based on the correspondence between the current state of the processing and the template type.

[0109] The regularly shaped edges of raster dots are helpful for estimating their position in a blurred image because only the edges from left to right (from background to foreground) are considered, which is easier to achieve at a comparative level.

[0110] Another implementation involves the edges 80 of the grating dots in the direction of the grating lines tending to form channels that are as straight as possible. This effect results in improved geometric stability of the grating image in the preferred direction, which can be used to align the grating image.

[0111] Therefore, it is advantageous to model the raster points so that they provide information for the alignment and originality encoding of the raster image.

[0112] In principle, it is feasible to use deconvolution methods within the scope of this invention to recover the shape of the raster points defined in the pre-printed original image, i.e., to reverse the soft edges created by printing. This is the reverse operation of convolution of image information, which is represented by the soft edges of the raster points. The raster image can be compared with the shape of the raster points obtained by deconvolution using different mathematical descriptors, such as based on centroid spacing functions, area functions, chord length functions, using quadratic matrix, or scale space based on curvature.

[0113] Figure 12 A schematic diagram of an image 210 with various zones and framing edges 211, 212 is shown, wherein an optional edge 213 is shown, which is not typically provided and should here only symbolize the edge of the image 210 shown as “empty space”.

[0114] Figure 12 A simplified version of the definitions of the framing edges 211 and 212, shown as dashed lines, is presented. The framing area 190 and the separate identification area 21 are shown as zones.

[0115] The framing area 190 has an entirely asymmetrical framing edge row number 222 of eight raster points forming the framing area 190 and a framing edge length of twelve raster points. In other words, the actual framing edge 212 has one to eight framing edge rows 222 on the framing area side, each framing edge row having a length predetermined by means of the framing edge length. The framing edge has the same framing edge length outside the image because it is preset by a restricted area, and the number of framing edge rows 224 is here optionally shown between one and three. Thus, for example, a framing edge area 225 of 12 by 3 raster points on both sides of the framing edge centerline 212 is obtained that should be evaluated by the authentication method. The evaluation does not have to be symmetrical, and the number of rows 224 and 222 can be chosen differently.

[0116] The identification or detection area 21 has eight raster points in the frame edge row number 222 and a frame edge length of twelve raster points, all of which are asymmetrical to form the detection area 21. The numbers here are the same as those for the area 190; however, they are not necessarily the same. In other words, the actual frame edge 211 has one to eight frame edge rows 222 on the detection area side, each with a length predetermined by the frame edge length. It has the same frame edge length outside the image because this is preset by the restricted area, and the frame edge row 224 is optionally shown between one and three. Thus, for example, a frame edge area 226 of 12 by 3 raster points on both sides of the frame edge centerline 212 is derived that should be evaluated by the authentication method. The framing edge 226 can also end at edge 213, and said edge is a wider horizontal framing edge 212 (not shown in the figures), because the outer region 210 of the image surrounding the detection area 21 is symmetrical and is also identified as symmetrical as an edge with a 100% grayscale black edge. However, the detection area 21 can also be located in the inner region of the image. The lengths or segments of the twelve asymmetrical raster points can be determined by an authentication method and can be used for the orientation and scaling of the overall image. The more framing edges 211, 212 used, the simpler, faster, and more accurate it is to precisely determine the detection area 21 pixel by pixel.

[0117] The viewfinder edges 225 and 226, i.e., the areas through which the raster dots pass, are exemplarily represented by a matrix (array) with a predetermined length and a predetermined width determined by the evaluation method. Figure 3B In the middle (for the two viewfinder edges 211 and 212) and in Figure 4C The middle (for the viewfinder edge 211 with a line width of three raster points on both sides and a length of thirty-six raster points) is also drawn exemplarily.

[0118] In summary, the present invention has several individual features, which in some respects are also independent technical teachings:

[0119] A method for identifying photocopies of black-and-white and color images, wherein

[0120] The features used for identification and authentication are hidden from the naked eye;

[0121] • Apart from dedicated tamper-proof indicators, orientation markers (position markers, alignment markers, synchronization markers) are invisible, as shown in Figure 3;

[0122] • The second piece of information may optionally be included in the feature;

[0123] Features, including orientation features, are inserted in the pre-printing process;

[0124] • Features based on interventions in the image raster, such as those shown in Figures 2, 4, and 5. Figure 10 and Figure 12 As stated;

[0125] • Demonstrate the juxtaposition of different raster dot groups and the blurring caused by the printing process of the original and the copy, such as from Figure 2D As the result was.

[0126] • The digitally generated print template is calculated based on a typical algorithm using the deformation caused by the printed original as a descriptor, and optionally trained for a suitable model for identifying the original; wherein the basis for the calculation is derived based on the characteristics of the printing ink, substrate or medium, such as a specific type of cardboard, so that the printer of the original can certify or specify the printing preset accordingly.

[0127] • Identification of originals and copies is performed using portable image inspection devices with suitable applications, such as smartphones with dedicated apps, where combinations of... Figure 8B Execute the method as described;

[0128] One advantage is that smartphones with cameras capable of average resolution can be used for object recognition, particularly by applying auxiliary methods to improve resolution, especially super-resolution and deconvolution, such as combining... Figure 8B , Figure 9 and Figure 11 As described.

[0129] List of reference numerals

[0130] 1. Raster dot shape

[0131] 2. Printed material with raster dots

[0132] 3. Copies of raster dots

[0133] 4. Raster dot shape

[0134] 5 raster dots print

[0135] 6-dot photocopy

[0136] 7. An assembly of grating units with irregularly shaped grating dots.

[0137] 8. Irregularly shaped grating dots

[0138] 9. Printed surface with eight detection zones

[0139] 10. Sub-surfaces with irregularly shaped grating dots (detection area or sampling area / synchronization area)

[0140] 11 Printing templates for natural or photographic images

[0141] 12 A partial view of image 11 showing a detection area with irregularly shaped grating dots.

[0142] 13. Enlargement of the digital template of the image

[0143] 14 Original printouts of images based on digital templates

[0144] 15. A photocopy of the image after scanning the original document.

[0145] 16. A partial view of the right eye portion of the original portrait image.

[0146] 17. A partial view of the right eye portion of the portrait image as the original print.

[0147] 18. A partial view of the right eye portion of a portrait image derived from a scanned copy.

[0148] 19 Orientation markers used to locate images and determine their orientation

[0149] 20. Synchronization markers (also orientation markers or alignment markers) used for image correction.

[0150] 21 Separate signage areas

[0151] 22. A raster-printed subface having raster points of a specific shape, such as irregularity, but different from the raster points according to 23.

[0152] 23. A raster-printed subface having raster points of a specific shape, such as circular, but different from the raster points according to 22.

[0153] 24-bit code

[0154] 25-bit code, such as 24, however, is derived from another order of the subfaces 22 and 23.

[0155] 26. A rasterized image with circular raster dots is used as the base image.

[0156] 27. A magnified view of the "mountain peak" in Image 26.

[0157] 28. A magnified view of the "bridge" in Image 26.

[0158] 29. Rasterized images with irregularly shaped raster dots.

[0159] 30. A magnified view of a local "peak" in Image 29.

[0160] 31. Enlarged view of the "bridge" in Image 29

[0161] 32. A local area in image 33 showing the outer edge with circular raster dots and the core region composed of irregularly shaped raster dots.

[0162] 33. Take images as the basic image.

[0163] 34. Grayscale illustration of the color image "Yellow Dove on Blue Background" as the basic image.

[0164] 35. A local area in image 34

[0165] 35a Sub-local in local 35

[0166] 35b: Selection box for the raster point shape in the selected portion of the cyan raster (as in 35a).

[0167] 36 Outline diagram of cyan raster dots

[0168] 37 digitally preset raster points

[0169] 38 printed raster dots

[0170] 39 digital raster points generated by scanning based on 38 raster points

[0171] 40 Printed raster dots based on the raster dots 39 generated from the scan.

[0172] 41 Printing Media

[0173] 42. Local area of ​​the image to be analyzed

[0174] 43 Spacing

[0175] 44 Waist

[0176] 45 Sensors

[0177] 46 Number Templates

[0178] 47. Soft Edge Method

[0179] 48 Soft-edged digital models

[0180] 49 Training of digital models

[0181] 50 training models

[0182] 51 Standardization of Training Models

[0183] 52 Matching Templates

[0184] 53 Comparative Inquiry

[0185] 54. Decision on Original or Copy

[0186] 55 Printouts to be inspected

[0187] 56. Super-resolution method steps

[0188] 57 High-resolution printed images

[0189] 58 Graphics Algorithms

[0190] 59 Datasets with Template Architecture

[0191] 60 Changes in camera parameters

[0192] 61 Image stack with images based on predetermined camera parameters

[0193] 62 Image Alignment

[0194] 63-aligned image stack

[0195] 64 Processing

[0196] 65 Results

[0197] 66 grating units

[0198] 67 Image recording chips in smartphones

[0199] 68 sensor pixel pitch

[0200] 69 raster element sampling

[0201] 70 resolution

[0202] 75 Macro Level

[0203] 76 Intermediate Version

[0204] 77 High-resolution version

[0205] 101 Raster element / Recorder element

[0206] 134. Yellow object (pigeon)

[0207] 135 Blue background

[0208] 136 tree-like cyan raster dots

[0209] 137 Magenta Linear Raster

[0210] 190 Scenic Area

[0211] The image around 210 serves as the base image.

[0212] 211 Shooting Edge

[0213] 212 Shooting Edge

[0214] 213 Edge

[0215] 222 Number of rows along the framing edge (side of the framing area)

[0216] 223 Frame edge length

[0217] 224 Number of frames (outside the image)

[0218] 225 Shooting Location (Shooting Area)

[0219] 226 Shooting Area (Exploration Area)

Claims

1. A printing method and authentication method for a printout (26) of a digital image to be created, comprising: A method for printing an authentication mark by applying an amplitude-modulated grating to an object in a detection area (21), wherein the printed surface of the detection area (21) comprises adjacent grating units, in which grating dots (1, 4, 8) are printed from a matrix of printable grating elements, wherein for multiple hue values ​​of the grating dots to be printed, a predetermined asymmetric matrix image is assigned to the printed image (2, 5) derived from the grating elements to be printed in a predetermined manner while the hue values ​​of the printed matter remain the same, wherein a predetermined number of grating dots in the detection area (21) are divided into a region (22) having an asymmetric grating dot shape and a region (23) having a symmetrical grating dot shape, wherein the regions are arranged in a matrix consisting of at least two rows (222) and two columns (223), wherein at least two non-parallel framing edges (211, 212) in at least one framing area (19, 20; 190) are printed to determine the position, boundaries, and orientation of the detection area (21). as well as A method for authenticating a printout (26) on a printed object, the method comprising: providing a portable image recording device having a microprocessor for implementing an authentication procedure; providing a printed image (16; 2, 5, 38) predetermined from and derived from print data for a predetermined number of raster dots of the printed object in a detection area (21); and providing a computer program for comparing the printed image predetermined from the raster dot data, wherein the method includes the additional steps of: recording an image of the printed object; identifying at least two framing edges (211, 212) of the at least one framing area (19, 20; 190) by the computer program for determining the detection area (21) in a raster dot-precise manner from the recorded image of the printed object; comparing the recorded printed image of the detection area (21) with the printed image (16; 2, 5, 38) predetermined from and derived from the print data by the computer program; and determining, based on the comparison, whether an original printout exists on the printed object.

2. The method according to claim 1, Each viewfinder edge (211, 212) along a predetermined segment of the printed image is composed of viewfinder edge areas (225, 226) of rows (222, 224) of grating points arranged side by side on both sides of the viewfinder edge (211, 212), wherein the differences between the grating points in the rows (222, 224) are selected from the group consisting of: symmetrical grating points relative to asymmetrical grating points, predetermined different grating angles of grating points, and AM modulation and FM modulation of grating points, wherein the differences can be preset differently or identically from the group for each viewfinder edge in a manner unrelated to each other.

3. The method according to claim 1 or 2, The framing area (190) defined by the framing edges (211, 212) has an asymmetrical raster dot shape, wherein the raster dots existing beyond the framing area (190) on the other side of the framing edges (211, 212) respectively form areas (23) with symmetrical raster dot shapes from the remaining printed image (210).

4. The method according to claim 1 or 2, The framing area (190) defined by the framing edges (211, 212) has a symmetrical raster dot shape, wherein the raster dots existing beyond the framing area (190) on the other side of the framing edges (211, 212) form areas (22) with asymmetrical raster dot shapes from the remaining printed image (210) or from the detection area (21).

5. The method according to claim 1 or 2, The framing area (190) defined by the framing edges (211, 212) has a symmetrical grating dot shape with a first grating angle, wherein the area beyond the framing area (190) is adjacent to a region with a second grating angle in the remaining printed image (210) or in the detection area (21) on the other side of the framing edges (211, 212).

6. The method according to claim 1 or 2, In multicolor printing, asymmetrical raster dots are placed in one of the two ink layers that should be printed last.

7. The method according to claim 6, The viewfinder edges (211, 212) are set by determining the shape of the raster dots and / or the raster angle of the same or different ink layers.

8. The method according to claim 1 or 2, The asymmetrical raster dots have gray values ​​between 25% and 75%.

9. The method according to claim 1 or 2, At least two of the framing edges (211, 212) are located at the corner points where the framing area (190) intersects with the framing edges (211, 212).

10. The method according to claim 1 or 2, One or more framing edges (211, 212) of one or more framing areas (19, 20, 190) are set at the edge of the printed image or in at least a pair of intersecting framing stripes.

11. The method according to claim 1 or 2, In the method for printing an authentication mark by applying amplitude-modulated raster printing in the detection area (21), a matching template (52) is generated based on printing data consisting of a group including printing substrate, printing ink and printing guide data.

12. The method according to claim 11, The matching template (52) is trained using the original printout and the strip proof, wherein in the method for authentication, the recorded image of the printed object is converted into the format of the matching template (52) for direct comparison (53) by a graphics algorithm.

13. The method according to claim 12, In the method used for authentication, the recording of the printed object's image includes recording multiple images using different camera parameters consisting of a group of focus changes and exposure time changes, in order to generate an image stack (61), the data of which is modified into an aligned image stack (63) for subsequent conversion into the format of the matching template (52).

14. The method according to claim 1 or 2, The predetermined matrix containing digital information (25) includes the distribution of the viewing area (19, 20, 190) and the detection area (21).

15. The method according to claim 11, The detection area (21) is checked by means of the matching template (52) based on the recorder elements (101) that make up the grating points (8) contained in the detection area, and the comparison includes a threshold of the consistency between the detected recorder elements (101) and the recorder elements (101) of the matching template (52).

16. The method according to claim 15, The system includes multiple separate detection zones (10, 21), and the total threshold obtained through all detection zones (10) or the single threshold of a single detection zone (10) is used as the basis for determination.

17. The method according to claim 1 or 2, The process begins with a digital template (46) of raster dots in pre-printing, followed by a soft-edge step (47), in which a soft-edge model (48) is generated from the digital template (46) based on data consisting of a print substrate, print ink, and print guide.

18. The method according to claim 17, The soft-edge model (48) is trained using the original printout or strip proof of the printed model (50) as a training step (49) to create a matching template (52) for image analysis of selected local areas of the printed image to be inspected. The matching (53) between the matching template (52) and the dataset (59) of the image to be authenticated provides a conclusion of "original" or "copy" after the application of the quality matrix (54).

19. The method according to claim 18, The graph algorithm (58) is used to convert the printout (55) to be inspected into a dataset (59) with the same architecture as the matching template (52).

20. The method according to claim 19, The mathematical form of the raster pattern is equivalent to a dense network of nodes aligned with the raster points of the printed image.

21. The method according to claim 19, Before applying the graphics algorithm (58), the print to be inspected (55) is detected by generating an image sequence using different camera parameters (60) consisting of a group including focus change, exposure time change and camera position change. The resulting image stack (61) is aligned in an alignment step (62) to obtain an alignment vector field. Other parameters varying between the images, consisting of the above group, are then obtained to obtain a result (65), which is processed by the graphics algorithm (58).

Images