Classifications

• • G—PHYSICS
  • G07—CHECKING-DEVICES
  • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
  • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
  • G07D7/20—Testing patterns thereon
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
  • G06F17/10—Complex mathematical operations
  • G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
  • G06F17/148—Wavelet transforms
• • G—PHYSICS
  • G07—CHECKING-DEVICES
  • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
  • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
  • G07D7/003—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency using security elements
• • G—PHYSICS
  • G07—CHECKING-DEVICES
  • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
  • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
  • G07D7/20—Testing patterns thereon
  • G07D7/2016—Testing patterns thereon using feature extraction, e.g. segmentation, edge detection or Hough-transformation

Definitions

• the present invention generally relates to the authentication of security documents, in particular of banknotes. More precisely, the present invention relates to further improvements of the invention disclosed in International Application No. WO 2008/146262 A2 of June 2 nd , 2008 entitled "AUTHENTIFICATION OF SECURITY DOCUMENTS, IN PARTICULAR OF BANKNOTES" (which claims priority of European Patent Applications Nos. 07109470.0 of June 1 st , 2007 and 07110633.0 of June 20 th , 2007) in the name of the present Applicant.
• the present invention was especially made with a view to further improve the invention disclosed in International Application No. WO 2008/146262 A2.
• a general aim of the invention is therefore to further improve the methods, uses and devices disclosed in International Application No. WO 2008/146262 A2.
• an aim of the present invention is to provide an improved method for checking the authenticity of security documents, in particular banknotes, which is more robust and can efficiently discriminate features printed, applied or otherwise provided on the security documents.
• the present invention is aimed at improving the discrimination between intaglio-printed textures and medium- or high-quality commercial offset printed textures.
• Yet another aim of the present invention is to provide such a method that can be conveniently and efficiently implemented in a portable device.
• a method for checking the authenticity of security documents in particular banknotes, wherein authentic security documents comprise security features printed, applied or otherwise provided on the security documents, which security features comprise characteristic visual features intrinsic to the processes used for producing the security documents, the method comprising the step of digitally processing a sample image of at least one region of interest of the surface of a candidate document to be authenticated, which region of interest encompasses at least part of the security features, the digital processing including performing a decomposition of the sample image by means of wavelet transform (WT) of the sample image.
• WT wavelet transform
• the decomposition of the sample image is based on a wavelet packet transform (WPT) of the sample image.
• the wavelet packet transform is a two-dimensional shift-invariant wavelet packet transform (2D-SIWPT), and is preferably based on an incomplete wavelet packet transform.
• decomposition of the sample image can include decomposition of the sample image into a wavelet packet tree comprising at least one approximation node and detail nodes, and looking for the detail node within the wavelet packet tree that has the highest information content.
• BBA best branch algorithm
• WPT wavelet packet transform
• a method for detecting security features printed, applied or otherwise provided on security documents comprising the step of digitally processing a sample image of at least one region of interest of the surface of a candidate document, which region of interest is selected to include at least a portion of the security features, the digital processing including performing a decomposition of the sample image by means of wavelet transform (WT) of the sample image.
• WT wavelet transform
• the decomposition of the sample image is similarly based on a wavelet packet transform (WPT) of the sample image.
• Figure 1 a is a greyscale scan of an exemplary banknote specimen
• Figure 1 b is a greyscale photograph of part of the upper right corner of the banknote specimen of Figure 1 a ;
• FIGS. 2a and 2b are enlarged views of the banknote specimen of
• Figures 3a and 3b are enlarged views of a first colour copy of the banknote specimen of Figure 1 a, Figure 3b corresponding to the area indicated by a white square in Figure 3a ;
• FIGS 4a and 4b are enlarged views of a second colour copy of the banknote specimen of Figure 1 a, Figure 4b corresponding to the area indicated by a white square in Figure 4a ;
• Figure 5 is a schematic illustration of a two-dimensional tree-structured Wavelet Packet Transform ("WPT") with three tree levels (two decomposition levels) ;
• WPT Wavelet Packet Transform
• FIG. 6 is a schematic illustration of a one-dimensional Shift Invariant Wavelet Packet Transform (“SIWPT”) implemented as a filter bank ;
• SIWPT Shift Invariant Wavelet Packet Transform
• Figure 7 shows the normalized histograms of wavelet coefficients of an intaglio (left) and a commercial (right) printed texture after a one-level 2D- SIWPT according to the invention ;
• Figure 8 shows an incomplete Wavelet Packet Tree decomposed according to a Best Branch Algorithm (BBA) in accordance with a preferred embodiment of the invention ;
• BBA Best Branch Algorithm
• Figure 9 illustrates six different printed textures that are characteristic of intaglio printing and that have been used as a basis to constitute a set of experiment samples ;
• Figure 10 is a diagram illustrating the inter-class and intra-class distance of the textures of Figure 9 printed by intaglio printing and by medium- and high- quality commercial offset printing ;
• Figure 11 is a two-dimensional feature space illustrating the classification of the samples after processing based on the variance ⁇ 2 and excess C of the statistical distribution of the wavelet coefficients resulting from the decomposition of the sample image according to the invention ;
• Figure 12 is a schematic diagram of a device for checking the authenticity of security documents according to the method of the present invention.
• the background of the present invention stems from the observation that security features printed, applied or otherwise provided on security documents using the specific production processes that are only available to the security printer, in particular intaglio-printed features, exhibit highly characteristic visual features (hereinafter referred to as "intrinsic features") that are recognizable by a qualified person having knowledge about the specific production processes involved.
• intrinsic features highly characteristic visual features
• the following discussion will focus on the analysis of intrinsic features produced by intaglio printing. It shall however be appreciated that the same approach is applicable to other intrinsic features of banknotes, in particular line offset-printed features, letterpress-printed features and/or optically-diffractive structures.
• intaglio-printed features are very well suited for the purpose of authentication according to the invention and furthermore give the best results. This is especially due to the fact that intaglio printing enables the printing of very fine, high resolution and sharply-defined patterns. Intaglio printing is therefore a preferred process for producing the intrinsic features that are exploited in the context of the present invention.
• FIG 1 a is a greyscale scan of an illustrative banknote specimen 1 showing the portrait of Jules Verne which was produced during the year 2004 by the present Applicant.
• This banknote specimen 1 was produced using a combination of printing and processing techniques specific to banknote production, including in particular line offset printing for printing the multicolour background 10 of the note, silk-screen printing for printing optically-variable ink patterns, including motifs of a planisphere 20 and of a sextant 21 , foil stamping techniques for applying optically-variables devices, including a strip of material 30 carrying optically-diffractive structures extending vertically along the height of the banknote (which strip 30 is schematically delimited by two dashed lines in Figure 1 a), intaglio printing for printing several intaglio patterns 41 to 49, including the portrait 41 of Jules Verne, letterpress printing for printing two serial numbers 51 , 52, and varnishing for varnishing the note with a layer of protective varnish.
• printing and processing techniques specific to banknote production including
• serial numbers 51 , 52 were printed and the varnishing was performed following the intaglio printing phase. It shall further be understood that the banknote specimen 1 was produced on sheet-fed printing and processing equipment (as supplied by the present Applicant), each printed sheet carrying an array of multiple banknote specimens (as is usual in the art) that were ultimately cut into individual notes at the end of the production process.
• Figure 1 b is a greyscale photograph of the upper right corner of the banknote specimen of Figure 1 a showing in greater detail the intaglio-printed logo of "KBA-GIORI" with the Pegasus 42 and tactile pattern 45 which comprises a set of parallel lines at forty-five degrees partly overlapping with the Pegasus 42.
• the characteristic embossing and relief effect of the intaglio printing as well as the sharpness of the print can clearly be seen in this photograph.
• Figure 2a is a more detailed view of a left-hand side portion of the portrait 41 of Figure 1 a (patterns 20, 21 and 44 being also partly visible in Figure 2a).
• Figure 2b is an enlarged view of a square portion (or region of interest R.o.l.) of the portrait 41 , which square portion is illustrated by a white square in Figure 2a.
• Figure 2b shows some of the characteristic intrinsic features of the intaglio patterns constituting the portrait 41.
• the region of interest R.o.l. used for subsequent signal processing does not need to cover a large surface area of the document. Rather, tests have shown that a surface area of less than 5 cm 2 is already sufficient for the purpose of the authentication.
• Figures 3a, 3b and 4a, 4b are greyscale images similar to Figures 2a, 2b of two colour copies of the banknote specimen shown in Figure 1 a, which copies were produced using commercial colour copying equipment.
• the depicted white square indicates the corresponding region of interest R.o.l. of the portrait which is shown in enlarged view in Figures 3b and 4b, respectively.
• the first colour copy illustrated in Figures 3a, 3b was produced using an Epson ink-jet printer and Epson photo-paper.
• the second colour copy illustrated in Figures 4a, 4b was produced using a Canon ink-jet printer and normal paper. A high-resolution scanner was used to scan the original specimen and provide the necessary input for the ink-jet printers.
• WO 2008/146262 A2 concerns a method defining how this difference can be brought forward and exploited in order to differentiate between the original and authentic specimen of Figures 2a, 2b and the copies of Figures 3a, 3b and 4a, 4b. The below discussion will deal with an improvement of this previous method.
• a wavelet is a mathematical function used to divide a given function or signal into different scale components.
• a wavelet transformation (or Wavelet Transform - hereinafter "WT") is the representation of the function or signal by wavelets. WTs have advantages over traditional Fourier transforms for representing functions and signals that have discontinuities and sharp peaks.
• Fourier transform is not to be assimilated to WT. Indeed, Fourier transform merely involves the transformation of the processed image into a spectrum indicative of the relevant spatial frequency content of the image, without any distinction as regards scale.
• Wavelet theory will not be discussed in depth in the present description as this theory is as such well-known in the art and is extensively discussed and described in several textbooks on the subject.
• the interested reader may for instance refer to [Mallatl 989] and [Unser1995] (see the list of references at the end of the present description).
• the pyramid structured WT discussed in [Mallatl 989] and the shift invariant WT discussed in [Unser1995] decompose successively the low frequency scales.
• a large class of textures has its dominant frequencies at the middle frequency scales.
• the present invention makes use of so-called
• WPT Wavelet Packet Transform
• banknotes are mainly produced by line offset, letterpress printing, foil application, and intaglio printing. Especially the latter technique plays a major role in banknote reliability (see [Dyck2008]).
• intaglio is of Italian origin and means "to engrave”.
• the printing method of the same name uses a metal plate with engraved characters and structures. During the printing process the engraved structures are filled with ink and pressed under huge pressure (tens of tons per inch) directly on the paper (see [vanRenesse2005]). A tactile relief and fine lines are formed, unique to intaglio printing process and almost impossible to reproduce via commercial printing methods (see [Schaede2006]).
• intaglio process is used to produce the currencies of the world, intaglio printing presses and the companies who own them are monitored by government agencies.
• WPT fine structures of intaglio technique
• a new feature extraction algorithm preferably based on incomplete WPT (see [Jiang2003]) is proposed. It belongs to the top-down approaches and can be applied to redundant shift invariant and shift invariant WPT.
• the algorithm decomposes the so-called Wavelet Packet Tree according to a criterion which is based on first order statistical moments of wavelet coefficients.
• the WPT is a generalization of the classical WT which means that not only the approximation (low frequency parts) but also the details (high frequency parts) of a signal are decomposed (see [Zhang2002]).
• all leaf nodes are decomposed in one approximation node Aj and three detail nodes cV ⁇ , h cHj, and cDij.
• cVij represents the vertical details, CHI J the horizontal details, and CDI J the diagonal details, where / is the decomposition level and j the node number.
• Both versions are downsampled and convulated by arbitrary wavelet filters g[n] and h[n].
• h[n] is a lowpass and g[n] is a highpass wavelet filter, respectively (see [Mallatl 989] and [Daubechies1992]).
• the version with the larger information content is identified on the basis of an information content criterion (which will be discussed hereinafter) and further decomposed whereas the other version is upcast.
• the upcasting yields to a nonredundant representation and to a fast execution time.
• the implementation of a one-dimensional SIWPT as filter bank is illustrated in Figure 6. As mentioned, in each tree level a nonshifted and a one-pixel-shifted version is decomposed and downsampled. On the basis of an information content criterion, one version is decomposed further, whereas the other version is discarded.
• the above-mentioned method was exclusively defined for one- dimensional signals.
• the SIWPT has been modified for two-dimensional signals such as images.
• the resulting two- dimensional SIWPT (or "2D-SIWPT") first decomposes four different shifted versions of the relevant node. Based on the resulting information content, three out of the four versions are discarded, whereas the version with the highest information content is further decomposed. According to experiments which were carried out, there is no difference is feature stability and quality between the shift invariant WPTs.
• the WPT enables an entire characterization of textures in all frequency scales.
• the number of nodes (or subimages) grows exponentially. This lowers the execution time considerably and a methodology has thus been devised to concentrate on the most relevant node only.
• the WPT is decomposed according to an information content criterion, resulting in an incomplete WPT.
• Most known methods like [Chang1993], [Jiang2003], [Coifman1992], [Saito1994], [Wang2008] and [Wang2000] use the entropy or the average energy of an image for this purpose. [Choi2006] applies the WT with first order statistics to classify different denominations of banknotes.
• Figure 7 shows the normalized histograms of wavelet coefficients of an intaglio (left) and a commercial (right) printed texture after a one-level 2D- SIWPT according to the invention (see also Figures 12 to 20 and the related description in International application No. WO 2008/146262 A2).
• the highly discontinuous structure of intaglio printing yields to a weighting on middle and high wavelet coefficients, whereas the histogram of commercial printing is narrowly distributed and weighted on small coefficients.
• the best separation between different printing techniques can be reached in this particular case, if the tree is decomposed towards variance and excess, until the subimage contrast is maximized. It can then be assumed that the relevant subimage represent the texture in the best possible way.
• textures could be influenced by additive noise. Taking into account, that noise is represented by small wavelet coefficients (see [Fowler2005]), the histograms of noisy textures are widely distributed.
• this subimage size should preferably be used as an overall stopping criterion.
• BBA Best Branch Algorithm
• the detail nodes contain the specific or detailed characteristics of a texture. Therefore, even if the textures are akin, they could be discriminated by this information.
• the approximation nodes of the most left tree branch, the so-called approximation branch contain only the low frequency information. Therefore, it is nearly impossible to distinguish different printing techniques with the information content of the approximation branch and such approximation branch should therefore not be used for feature extraction.
• Theoretically their children, which represent the lower part of the middle frequency scales, could yield the best spatial frequency resolution. This information could not be directly extracted out of the approximation nodes. For this reason the approximation nodes have to be decomposed as long as their children give the best spatial frequency resolution of the whole tree.
• Branch Algorithm Algorithm 1 Best Branch Algorithm
• ⁇ best node is part of the detail branch ⁇
• Figure 8 schematically shows an illustration of an incomplete Wavelet Packet Tree which has been decomposed using the above Brest Branch Algorithm.
• the highlighted nodes are identified to be the best nodes (cBi, cB ⁇ , cB ⁇ ) of their corresponding decomposition level and the dashed nodes are those which have been discarded during decomposition.
• the detail branch of the third decomposition level characterizes the texture almost optimally.
• further decomposition of the approximation node Ai,o leads to identification of the best node cB ⁇ for the second decomposition level.
• further decomposition of the approximation node M,o accordingly leads to decomposition into nodes Azo, cV3,i, cti3,2, and cD3,3 which are subsequently discarded as shown in dashed lines.
• Figure 8 shows that further decomposition of the best node CB3 of the third decomposition level does not lead to a more optimal representation of the feature and decomposition is accordingly stopped. As a result, the detail branch of the third level is selected for feature extraction.
• the set consists of six different textures with an image size of 256x256 pixels as illustrated in Figure 9. As shown in Figure 9, the textures differ in contrast, latitude of gray-scale transitions and structure. They are illustrative of the most common security printing structures produced by intaglio printing.
• Figure 10 shows the inter- and intra-class distance between the intaglio-printed textures and the medium- and high-quality commercial offset printed textures, respectively, for the first three decomposition levels (indicated on the horizontal axes in Figure 10).
• the dashed line highlights the corresponding decomposition level where the Best Branch Algorithm has stopped the decomposition.
• the Best Branch Algorithm has stopped the decomposition at the level which achieves the best inter-class distance between the intaglio-printed textures and the medium- or high-quality commercial offset printed textures with a rate of 100%.
• the corresponding intra- class distance of the medium- and high-quality commercial offset printed textures is minimized in most cases with a rate of approximately 60%.
• the intra-class distance is not minimized for all of the 900 investigated textures, it can be observed that the classes are narrowly distributed. Therefore, on average, the BBA stops at the level where the classes are best separated and lowest expanded.
• Figure 10 demonstrates that the Best Branch Algorithm (BBA) stops the decomposition for all 900 investigated textures at the level where they are best characterized. In this way, the best inter-class distance is reached with a rate of 100%. Even if the intra-class distance does not reach its minimum in all cases, the class clusters are still narrowly distributed as schematically illustrated in Figure 11.
• BBA Best Branch Algorithm
• Figure 11 shows a two-dimensional feature space where the relevant textures have been classified on the basis of their variance ⁇ 2 (along the horizontal axis in Figure 11 ) and the excess C (along the vertical axis in Figure 11 ) of the distribution of the wavelet coefficients resulting from the decomposition using the BBA.
• the circles on the lower-right corner of Figure 11 designate the medium- quality commercial offset printed textures, while the diamonds on the lower middle portion of Figure 11 designate the high-quality commercial offset printed textures.
• the squares on the upper-left corner of Figure 11 designate the intaglio-printed textures.
• Figure 11 shows that the BBA stops on average at the level where the classes are best separated and lowest expanded. This enables a simple separation of the various class clusters using linear boundaries.
• N MxM is the size of the texture image, which execution time is perfectly suitable for a practical implementation, for instance on a Field Programmable Gate Array (FPGA).
• FPGA Field Programmable Gate Array
• the skewness of the statistical distribution of the wavelet coefficients - also referred to in statistics as the "third moment" - which is a measure of the asymmetry of the statistical distribution ;
• the statistical entropy which is a measure of changes in the statistical distribution.
• FIG. 21 schematically illustrates an implementation of a device for checking the authenticity of security documents, in particular banknotes, according to the above-described method.
• This device comprises an optical system 100 for acquiring a sample image (image c°) of the region of interest
• DSP digital signal processing
• the DSP 200 may in particular advantageously be implemented as a Field-Programmable-Gate-Array (FPGA) unit.
• FPGA Field-Programmable-Gate-Array
• the device of Figure 12 may in particular be embodied in the form of a portable electronic device with integrated image-acquisition capability such as a smart phone.
• the classifying features may conveniently be statistical parameters selected from the group comprising the arithmetic mean, the variance ( ⁇ 2 ), the skewness, the excess (C), and the entropy of the statistical distribution of the wavelet coefficients resulting from the decomposition of the sample image.
• the method may provide for the determination of an authenticity rating of a candidate document based on the extracted classifying features.
• Such an authenticity rating computed according to the above described method can be optimised by designing the security features that are to be printed, applied, or otherwise provided on the security documents in such a way as to optimise the authenticity rating of genuine documents.
• Such optimisation can in particular be achieved by acting on security features including intaglio patterns, line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
• security features including intaglio patterns, line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
• a high density of such patterns preferably linear or curvilinear intaglio-printed patterns, as shown for instance in Figure 2b, would in particular be desirable.
• the authentication principle is preferably based on the processing of an image containing (or supposed to be containing) intaglio-printed patterns
• the invention can be applied by analogy to the processing of an image containing other security features comprising characteristic visual features intrinsic to the processes used for producing the security documents, in particular line offset patterns, letterpress patterns, optically-diffractive structures and/or combinations thereof.
• each region of interest is preferably selected to include a high density of patterns, preferably linear or curvilinear intaglio-printed patterns as shown for instance in Figure 2b (see also Figure 9).

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