EP2422326A1 - Procédé pour détecter des tentatives de manipulations d'un terminal libre-service et unité de traitement de données associé - Google Patents

Procédé pour détecter des tentatives de manipulations d'un terminal libre-service et unité de traitement de données associé

Info

Publication number
EP2422326A1
EP2422326A1 EP10717089A EP10717089A EP2422326A1 EP 2422326 A1 EP2422326 A1 EP 2422326A1 EP 10717089 A EP10717089 A EP 10717089A EP 10717089 A EP10717089 A EP 10717089A EP 2422326 A1 EP2422326 A1 EP 2422326A1
Authority
EP
European Patent Office
Prior art keywords
image
atm
camera
processing unit
data processing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP10717089A
Other languages
German (de)
English (en)
Inventor
Christian Reimann
Holger Santelmann
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wincor Nixdorf International GmbH
Original Assignee
Wincor Nixdorf International GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wincor Nixdorf International GmbH filed Critical Wincor Nixdorf International GmbH
Publication of EP2422326A1 publication Critical patent/EP2422326A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F19/00—Complete banking systems; Coded card-freed arrangements adapted for dispensing or receiving monies or the like and posting such transactions to existing accounts, e.g. automatic teller machines
    • G07F19/20—Automatic teller machines [ATMs]
    • G07F19/207—Surveillance aspects at ATMs
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07F—COIN-FREED OR LIKE APPARATUS
    • G07F19/00—Complete banking systems; Coded card-freed arrangements adapted for dispensing or receiving monies or the like and posting such transactions to existing accounts, e.g. automatic teller machines
    • G07F19/20—Automatic teller machines [ATMs]

Definitions

  • the invention relates to a method for detecting manipulation attempts on a self-service terminal according to the preamble of claim 1. Furthermore, the invention relates to a device operating according to the method, in particular a data processing unit for processing image data, as well as a self-service terminal equipped therewith, in particular a self-service terminal designed as an ATM ,
  • the spied out Data is then transferred to a remote receiver via a transmitter built into the keyboard overlay, or stored in a data memory located in the keyboard overlay.
  • a transmitter built into the keyboard overlay or stored in a data memory located in the keyboard overlay.
  • Many of today's skimming devices are very difficult to distinguish with the human eye from original controls (keyboard, card reader, etc.).
  • monitoring systems which have one or more cameras which are mounted in the area of the location of the self-service terminal and detect the entire control panel and often also the area of residence of the user.
  • Such a solution is described for example in DE 201 02 477 Ul.
  • the local camera monitoring By means of the local camera monitoring, both the control panel itself and the user's area in front of it can be detected.
  • a sensor is provided in order to distinguish whether a person is in the occupied area.
  • devices and methods for detecting tampering attempts on a self-service terminal are basically known, a camera being mounted on at least one of the elements provided in the control panel, e.g. Keyboard, cash dispenser, etc., is aligned and wherein the image data generated by the camera are evaluated.
  • a camera being mounted on at least one of the elements provided in the control panel, e.g. Keyboard, cash dispenser, etc., is aligned and wherein the image data generated by the camera are evaluated.
  • Object of the present invention is therefore to provide a solution for a reliable and inexpensive to implement Propose camera monitoring with detection of tampering attempts.
  • the object is achieved by a method having the features of claim 1 and by a data processing unit operating thereafter and a self-service terminal equipped therewith.
  • At least one edge image is created from the image data generated by the camera by means of edge detection, and that the edge image is evaluated by means of a reference edge image.
  • edge detection not only causes a significant data reduction, but also increases the speed and reliability of the image analysis.
  • edge image data representing the edge image with reference edge image data representing the reference edge image is logically linked to first result image data representing a first result image, in particular by an exclusive-OR operation.
  • This data operation causes all the edges that coincide with the reference edge image to be hidden in this result image thus assembled, so that essentially only the edges or the elements or parts outlined therefrom that could be manipulated are visible.
  • the first result image data preferably with the reference edge image data become logical to second result image data
  • the second result image represent, in particular linked by an AND operation. This operation hides the areas that are not to be monitored so that only those edges or parts of them that belong to foreign objects that have been inserted into the area to be monitored are visible. These are in particular superstructures, spy cameras and similar manipulations.
  • the evaluation of the edge images can be realized very efficiently and quickly even with simple computer hardware and software, if in the second result image of the white component is determined and if it is checked to detect a manipulation attempt, if the white component is a vorgebaren threshold exceeds.
  • the respective edge image is calculated from a plurality of individual images, wherein in particular an average image is calculated by means of the formation of average values from the respective image data. These steps are performed to inter alia. to have as low-noise image data for the actual evaluation.
  • the reference edge image is also calculated from a plurality of reference individual images.
  • an average image is also calculated in particular by means of the formation of average values from the respective image data.
  • the Average values each determine the average color value for each pixel.
  • the respective average image is converted into a grayscale image.
  • Sobel filtering of the image data is preferably carried out for the actual edge detection, wherein in particular the respective gray scale image is subjected to Sobel filtering in order to produce the edge image or the reference edge image.
  • a combined Sobel filter in a normalized form e.g., 3x3 horizontal and 3x3 vertical may be used.
  • the edge detection is performed by means of a segmentation filtering of image data, wherein in particular the respective grayscale image subjected to Sobel filtering is subsequently subjected to segmentation filtering in order to produce the edge image or the reference edge image.
  • the edge image is decomposed by means of a threshold into its black and white parts, so that a mask of the edges is formed.
  • a manual image reworking then takes place, wherein in particular the respective gray scale image subjected to the segmentation filtering is subjected to a manual image reworking in which unimportant image elements are removed for the evaluation such as non-monitored areas or edges or artifacts caused by image noise.
  • unimportant image elements such as non-monitored areas or edges or artifacts caused by image noise.
  • a data processing unit implementing the method, e.g. as a PC, and a self-service terminal equipped therewith.
  • the recognition of superstructures on individual or several elements can be significantly improved and fully automated by the invention.
  • the camera detects the elements which are particularly suitable for manipulation and / or the elements arranged in particularly manipulation-suitable areas of the control panel, such as cash dispenser, keyboard, card slot and / or screen.
  • the elements are therefore preferably control elements in the strict sense, but can also be other elements, such as storage space in the operating area or an applied logo, signage lettering and the like.
  • the camera has a detection angle, which preferably detects multiple controls, such as the cash dispenser and the keyboard.
  • the camera preferably has a wide-angle lens with a coverage angle of at least 130 degrees. It can be advantageous if the camera is installed in that housing section of the self-service terminal which limits the control panel laterally or upwardly. This can be in particular the frame of the control panel.
  • the data processing unit connected to the at least one camera can be completely integrated in the self-service terminal.
  • the data processing unit has a first stage for image processing, in particular for shadow removal, edge detection, vectorization and / or segmentation, which receives the image data.
  • the data processing unit may have a second stage downstream of the first stage for feature extraction, wherein in particular a so-called blob analysis, edge position and / or color distribution is performed.
  • a third stage downstream of the second stage can be provided for classification.
  • the data processing unit if it detects a manipulation attempt on the detected elements by means of processing the image data, triggers an alarm, blocks the self-service terminal and / or triggers an additional camera (portrait camera).
  • the camera and / or the data processing unit is deactivated during the operation and / or maintenance of the self-service terminal.
  • Fig. 1 shows a flowchart of the method according to the invention
  • Figures 2 a) -d) show examples of generated edge images and result images
  • Fig. 3 a) -d) show examples of original recorded camera images and edge or result images
  • Fig. 4 shows a perspective view of the
  • Fig. 5 represents the detection range of the camera of Fig. 4;
  • Fig. 7 shows a block diagram of a data processing unit connected to the camera and a video surveillance unit connected thereto.
  • FIGS. 2 and 3 would actually have to represent white edge curves on a black background. To meet the requirements for patent drawings, these representations are inverted here displayed, ie black edge gradients are displayed on a white background.
  • FIG. 1 shows a schematic representation of a sequence diagram of the method 100 according to the invention, which can be subdivided into the following step sequences 110 to 130:
  • step sequence 110 with the individual steps 111 to 115, at least one reference edge image is generated from the camera image data. It is assumed that an unmanipulated state self-service terminals.
  • step sequence 120 with the individual steps 121 to 124, at least one edge image is generated from the camera image data.
  • the self-service terminal is in use, so that a manipulation attempt could be carried out, which is to be recognized by the method described here.
  • the at least one edge image is evaluated with the aid of the at least one reference edge image.
  • FIGS. 2a) -d) and FIGS. 3a) -d) show examples of the images produced and further processed in the method.
  • FIGS. 4 to 7 show a perspective view of the basic structure of a self-service terminal in the form of an ATM ATM, which has a control panel CP and is equipped with a camera CAM according to the invention for detecting attempts at manipulation.
  • the camera CAM is located in a lateral housing part, which surrounds or surrounds the control panel of the ATM ATM.
  • To the control panel include in particular a cash dispenser 1, also called shutter, and a keyboard 2.
  • the detection range or angle of the camera CAM comprises at least these two elements 1 and 2 and enables a reliable detection of such manipulation attempts.
  • FIG. 5 shows the detection range of the camera CAM from the camera viewing angle.
  • the camera is equipped with a wide-angle lens to capture at least these two elements or subregions of the control panel.
  • the ATM ATM is designed so that the said elements 1 and 2 preferably have the most homogeneous surfaces with delimiting edges. This simplifies object recognition. By attaching the camera CAM at this particularly suitable position, the subregions or elements 1 and 2 can be optically measured very reliably. It can be provided that the camera is focused in particular on certain areas. An alternative positioning of the camera is illustrated with reference to FIG. FIG.
  • FIG. 6 illustrates the detection field of a camera, which is similar to the camera CAM, but which is now installed in the upper area of the ATM ATM and detects the control panel CP from above.
  • other elements can be provided in the detection range of the camera, such as a shelf near the keyboard, a card input funnel 4, ie a feed part for the card reader, and eg a screen 5 or display.
  • These further mentioned elements 3, 4 and 5 represent potential targets for manipulation attempts.
  • the camera has optics optimized for this application as well as a resolution of, for example, 2 megapixels and more.
  • the camera CAM is connected to a special data processing unit 10 (see FIG. 7).
  • This data processing unit described later, makes it possible to optimally evaluate the image data generated by the camera so as to make a tampering attempt, such as tampering, with high certainty.
  • a superstructure of the keyboard 2 to recognize immediately and possibly trigger alarms and deactivations.
  • a superstructure to the cash box (shutter) and / or attaching items for receiving security information, in particular PIN no.
  • security information in particular PIN no.
  • an optical measurement of the detected elements, such as the keyboard 2 is carried out within the data processing unit 10 with the aid of the camera CAM in order to be able to clearly recognize deviations in the case of manipulation. Tests by the applicant have shown that even reference deviations in the millimeter range can be clearly recognized.
  • the invention is particularly suitable for detecting foreign objects (superstructures, scouting camera, etc.) and in particular comprises an edge detection, which is optionally combined with a segmentation in order to be able to clearly and reliably recognize the contours of foreign objects in the control panel.
  • the required image data processing takes place predominantly in the data processing unit described below.
  • FIG. 7 shows the block diagram of a data processing unit 10 according to the invention, to which the camera CAM is connected, as well as a video surveillance or CCTV unit 20, which is connected to the data processing unit 10.
  • the data processing unit 10 has, in particular, the following stages or modules, which are to be understood here as logical blocks, in which the aforementioned step sequences of the method (see 110 to 130 in FIG.
  • a first stage 11 data processing 10 executes the step sequence 110 for generating at least one reference Edge image REF (see also Fig. 1 and 2a).
  • an average image is calculated from a plurality of individual images in a first step 111.
  • the individual binders originate, for example, from a video stream which the camera CAM made after the installation of the ATM before the actual start-up, ie in an unmanipulated state.
  • the calculation of an average image whereby, for example, the average color value is calculated on a pixel-by-pixel basis, causes a noise suppression of the image noise occurring in the individual images.
  • a grayscale image is generated from the color average image.
  • edge detection is performed by Sobel filtering (eg, 3x3 horizontal, 3x3 vertical) to obtain a first reference edge image.
  • Sobel filtering eg, 3x3 horizontal, 3x3 vertical
  • a segmentation filter is applied in step 114, in which this first reference edge image is decomposed into its black and white components by means of a threshold value.
  • This second image is preferably improved in an optional step 115 by manual image processing.
  • disturbing picture elements which are not relevant for the later evaluation, are removed by hand. These are, for example, edges of a region that is not to be monitored or virtual edges or artifacts that have arisen due to image noise and the like.
  • This reference edge image REF reproduces the relevant edges in the view of the camera CAM (see also FIG. 5).
  • the edge images shown in FIGS. 2 and 3 should actually represent white edge curves on a black background. In order to meet the requirements for patent drawings, these representations are reproduced here in an inverted manner, ie black edge gradients are displayed on a white background.
  • a second stage 12 at least one edge image EM (see FIG.
  • the steps 121 to 124 are carried out, which are configured analogously to the steps 111 to 114. Accordingly, a colored average image is calculated in step 121 from a plurality of individual images recorded under real conditions.
  • a grayscale image is generated from this, which is then subjected to edge detection in step 123.
  • Sobel filtering is used, with a segmentation filter subsequently being used in step 124.
  • This segmented edge image EM is shown in FIG. 2b) (see also FIG. 5) and is used for the actual image evaluation.
  • this actual evaluation and detection of manipulation attempts now takes place on the basis of the step sequence 130 (see FIG.
  • the segmented edge image EM is logically linked to the reference edge image REF by an exclusive-OR operation (XOR).
  • XOR exclusive-OR
  • this first result image R1 is logically linked to the reference edge image REF by an AND operation (AND).
  • AND AND
  • this second result image R2 which is characterized in particular by the fact that areas not to be monitored are hidden (compare with FIG. 2a / b / c). Accordingly, this second result image R2 contains essentially only those edges that could be changed with respect to the reference and could indicate a manipulation attempt.
  • FIG. 2d shows a result image R2, which contains more or less no more noticeable edges and thus does not indicate a manipulation attempt.
  • FIG. 3c) again shows this result image R2 (edge image) and
  • FIG. 3d shows a result image R2 * (edge image) which was likewise obtained by the above-described data evaluation (step sequence 130) and contains very noticeable edges, which indicate a successful manipulation attempt.
  • Fig. 3b) shows the corresponding output image, so the representation of the original camera image (no edge image). The manipulation can be recognized on both images (FIG. 3b / d), namely that a superstructure has been attached to the ATM.
  • step 133 is carried out, in which the result image R2 or R2 * is examined for its white component. If a predefinable threshold value is exceeded, the high proportion of white indicates many manipulated edges. If so, it can System trigger a protection function (automatic alarm, locking the ATM, etc.).
  • the stage 13 is in turn connected to an interface 14 via which various alarm or monitoring devices can be activated or addressed.
  • the steps 11 and / or 12, which serve for image processing, may be connected to a second interface 15 via which a connection to the CCTV unit 20 is established. With the help of this CCTV unit, for example, a remote monitoring or remote diagnosis can be performed.
  • the data processing unit 10 is responsible for processing the image data D generated by the camera CAM.
  • the image data D first arrive at the first stage 11 or second stage 12, which generate edge images from the incoming image data, wherein besides the actual edge detection also measures such as shadow removal, vectorization and / or segmentation can be performed.
  • a feature extraction in particular in stage 12, which can be carried out, for example, by means of a so-called blob analysis, an edge positioning and / or a color distribution.
  • blob analysis is used to detect contiguous regions in an image and to take measurements on the blobs.
  • a Blob (Binary Large Object) is an area of adjacent pixels with the same logical state. All the pixels in a picture belonging to a blob are in the foreground. All other pixels are in the background. In a binary image, pixels in the background have values that equal zero, while every pixel other than zero is part of a binary object.
  • the actual evaluation it is also possible to provide a classification which, on the basis of the extracted features, determines whether hostile manipulation has occurred at the self-service terminal or ATM ATM or not.
  • the data processing unit 10 can be realized, for example, by means of a personal computer which is connected to the ATM ATM or which is integrated therein.
  • an additional camera CAMO can also be mounted on the ATM ATM (see Fig. 4), which is directed to the user or customer and in particular detects his face.
  • This additional also known as a portrait camera, camera CAMO can be triggered upon detection of a tampering attack to make a picture of the person located at the ATM. For example, once a skimming attack is detected, the described system can perform the following actions:
  • the size and nature of the actions taken or countermeasures can be configured by the operator of the ATM via the system described here.
  • a plurality of cameras can also be provided there, wherein a first camera detects the control panel from outside, a second camera e.g. the card entry funnel from inside detected.
  • a third camera corresponding to said portrait camera may be provided.
  • the camera CAM on the control panel and possibly also a camera in the card input are used.
  • the portrait camera CAMO is also used.
  • all cameras have a resolution of at least 2 megapixels.
  • the lenses used have a viewing angle of about 140 degrees and more.
  • the exposure time of the cameras used in a wide range for example, 0.25 msec. freely adjustable up to 8000 msec (8 sec.). This allows adaptation to a wide variety of lighting conditions.
  • Applicant's experiments have shown that a camera resolution of about 10 pixels per degree can be achieved. Based on a distance of one meter, an accuracy of 1.5 mm per pixel can be achieved. This in turn means that a manipulation from a reference deviation of already 2 to 3 mm safely detected can be. The closer the camera lens is to the detected element or object, the more accurate the measurement can be. Thus, in closer areas even an accuracy of less than 1 mm can be achieved.
  • the camera CAM mounts rather in the side case of the ATM ATM or in the upper housing area. Also arise depending on the camera position different monitoring options. When monitoring the various elements or subareas, the following is achieved in particular:
  • the detection of the cash dispenser (shutter) 1 makes it possible to check manipulations in the form of so-called cash trappers, i. special superstructures.
  • the detection of the keypad makes it possible to determine there manipulation attempts by superstructures or changes to light protection measures and the like.
  • the detection of the support surface makes it possible in particular to detect complete overbuilding.
  • the detection of the card input funnel 4, in particular by a camera integrated therein, makes it possible to detect local manipulations.
  • Deviations at the rear outer edge of the support surface can be detected as early as 4 mm. Deviations at the lower edge of the shutter can already be detected from 8 mm.
  • An optional connection of the system to the Internet via the interface 23 makes it possible to remotely control the camera or the various cameras.
  • the acquired image data can also be transmitted via the Internet connection to a video server.
  • the respective camera virtually acts as a virtual IP camera.
  • the above-described CCTV unit 20 serves such a video surveillance facility, wherein the interface 15 to the CCTV unit is designed for the following functions:
  • the system is designed so that no false alarms are generated by hands and / or objects in the picture during normal operation (eg withdrawing money, checking account balance, etc.). Therefore, the tamper detection is disabled during the period of normal machine use. Also, in time periods in which, for example, a cleaning or a short-term other use (storage of account statements, interactions before and after the start of a transaction) are not used for tamper detection. Essentially, therefore, only rigid and immovable manipulation attempts are preferably analyzed and recognized.
  • the system is designed to work in a wide variety of lighting conditions (day, night, rain, cloudy, etc.). Also be changing light conditions, such as light reflections, shadows and the like, in the Image processing compensated or ignored to avoid a false alarm. In addition, technically occurring events, such as the failure of a lighting and the like, are taken into account. These and other special cases are recognized and solved in particular by the third stage for classification.
  • the system described herein is preferably modular in design to allow for different configurations.
  • the actual image processing and the CCTV connection are realized in different modules (see FIG. 7).
  • the system presented here is also suitable for documenting the detected manipulations or digitally archiving them.
  • the captured images are provided with corresponding meta information, such as. Timestamp, type of manipulation, etc., stored on a hard disk in the system or in a connected PC.
  • messages may be forwarded to a platform, e.g. Error messages, status messages (deactivation, mode change), statistics, suspected manipulation and / or alarm messages.
  • a corresponding message with the respective alarm level can be forwarded to the administration interface or the interface.
  • the following options are also implemented at this interface:
  • Query camera data such as number of cameras, construction status, serial number, etc.,temperaturstarnmchal or setting of camera parameters and / or registration for alarms (notifications).
  • the invention presented here is particularly suitable for reliably detecting hostile manipulations on a self-service terminal, such as at an ATM.
  • the control panel is continuously and automatically monitored by at least one camera.
  • an image data processing which includes an edge detection
  • the elements detected by the camera are optically measured in order to detect deviations from reference data. It has been shown that even deviations in the millimeter range can be reliably detected.
  • a combination of edge detection and segmentation is preferably used, so that contours of left objects can be clearly recognized and marked. In the case of a manipulation attempt countermeasures or actions can be triggered.
  • the present invention has been exemplified by
  • ATMs are described, but not limited to, but can be applied to any type of self-service terminal.
  • ATM self-service terminal designed as an ATM, with a control panel CP, ia. having:
  • I cash dispenser 2 keypads, 3 shelves, 4 card slots, 5 screens
  • Video surveillance or CCTV unit with:

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  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
  • Closed-Circuit Television Systems (AREA)
  • Alarm Systems (AREA)

Abstract

L'invention concerne un procédé (100) pour détecter des tentatives de manipulations d'un terminal libre-service, notamment de distributeurs automatiques de billets, selon lequel est prévu un tableau de commande qui abrite des éléments, par exemple un clavier, un casier de délivrance de billets etc., une caméra étant orientée sur au moins un des éléments et les données d'image générées par la caméra étant évaluées. A partir des données d'image générées, au moins une image de contours est créée (étape 120) par une détection de contours. L'image de contours est évaluée (étape 130) à l'aide d'une image de contours de référence. Pour créer l'image de contours de référence, plusieurs images individuelles sont prises en compte (étape 110). La détection de contours est une évaluation entièrement automatique et permet la détection de tentatives de manipulations.
EP10717089A 2009-04-22 2010-04-16 Procédé pour détecter des tentatives de manipulations d'un terminal libre-service et unité de traitement de données associé Withdrawn EP2422326A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102009018320A DE102009018320A1 (de) 2009-04-22 2009-04-22 Verfahren zum Erkennen von Manipulationsversuchen an einem Selbstbedienungsterminal und Datenverarbeitungseinheit dafür
PCT/EP2010/055016 WO2010121959A1 (fr) 2009-04-22 2010-04-16 Procédé pour détecter des tentatives de manipulations d'un terminal libre-service et unité de traitement de données associé

Publications (1)

Publication Number Publication Date
EP2422326A1 true EP2422326A1 (fr) 2012-02-29

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US (1) US9165437B2 (fr)
EP (1) EP2422326A1 (fr)
CN (1) CN102414725A (fr)
DE (1) DE102009018320A1 (fr)
WO (1) WO2010121959A1 (fr)

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US20120038774A1 (en) 2012-02-16
US9165437B2 (en) 2015-10-20
WO2010121959A1 (fr) 2010-10-28
CN102414725A (zh) 2012-04-11
DE102009018320A1 (de) 2010-10-28

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