DE102021111816A1 - Traceable image processing - Google Patents

Abstract

Method (100) for processing one or more source images (1) to form at least one target image (3), with the steps:
• the at least one target image (3) is generated (110) by applying a predetermined processing function (2) to the source image or images (1);
• by applying a predetermined hash function (4), a hash value (1#) of each source image (1) is formed (120) that contributed to the target image (3);
• Validation information (5) for the origin of the target image (3) is determined from the hash value (1#) or the hash values of the source image or images (1) and an identification (2a) of the processing function (2). (130);
• the validation information (5) is stored (140) in association with the target image (3).
Associated method (200) for checking the integrity of a target image (3).

Description

The invention relates to the digital processing of images, in particular for security-relevant applications, such as for monitoring areas to be protected or controlling vehicles.

State of the art

When monitoring areas to be protected, images recorded with one or more cameras are the most important source of information and, if necessary, also the most important piece of evidence. In the era of analogue image recording, it was comparatively difficult and time-consuming to falsify the recordings without being detected. Advances in digital imaging have significantly reduced the level of effort and skill required for such counterfeiting. There is already software on the market that enables even inexperienced users to retouch disturbing objects from an image within a few seconds.

In other applications, too, such as in science, the question of how to make it more difficult for alleged successes to be "substantiated" with manipulated or fictitious images in order to fraudulently obtain merits is becoming more urgent.

From the DE 10 2018 126 533 A1 an authentication module for sensor data is known, with which subsequent manipulation of sensor data can be made more difficult.

task and solution

It is the object of the invention to make the processing of images comprehensible so that unauthorized manipulations, such as the smuggling or suppression of information, are made more difficult.

This object is achieved according to the invention by a method for processing one or more images according to the main claim and by a method for checking the integrity of an image according to the additional claim. Further advantageous refinements result from the subclaims which refer back thereto.

Disclosure of Invention

As part of the invention, a method for processing one or more source images to form at least one target image was developed.

This destination image is created by applying a predetermined editing function to the source image(s). By applying a predetermined hash function, each source image that contributed to the destination image is hashed.

Any function that maps a source image to a hash value that usually contains many times less information than the source image itself can be used as a hash function. Typical hash values have a length of the order of 64 bytes, while the source images can be many megabytes in size. Cryptographically secure hash functions are used particularly advantageously. These hash functions are distinguished by the fact that it is only possible with a great deal of effort to find a modified image for a given source image, which image is mapped to the same hash value by the hash function.

Validation information for the origin of the target image is determined from the hash value or hash values of the source image or images and an identification of the processing function. This validation information is stored in association with the target image.

In this way, a given image can be checked against the validation information as to whether it was generated from one or more claimed source images and/or using one or more claimed editing functions. For example, hash values of the claimed source images can be compared with hash values in the validation information. These source images can then be processed with the claimed editing functions, for example, and it can be checked whether the result is consistent with the specified image.

For example, it can be determined in this way that a specified image was only generated using processing functions that only improve the quality and/or recognisability of the image, but without changing the semantic image content. These editing functions include, for example, adjusting the brightness and contrast, other color corrections, eliminating blur caused by defocusing or movement, or removing noise from the image. A change in the semantic image content, on the other hand, occurs in particular if one or more objects visible in the image are removed, made unrecognizable, exchanged for other objects and/or modified in such a way that they simulate the appearance of a different object.

It can also be determined, for example, that when several source images are combined to form a target image, on the one hand none are provided n source images have been suppressed and on the other hand no additional source images have been smuggled in. For example, when source images are transmitted over a network to be assembled into a destination image, an attempt might be made to disrupt or alter the transmission of one of the source images, and/or a source image entirely for another image exchange.

Storing the validation information in association with the target image can be done in any way that makes it difficult for an attacker to modify the target image in an undesired way and to align the validation information with this undesired modification.

For example, the validation information can be merged with the target image. This has the advantage that the validation information is available with the target image and does not have to be obtained from an external source. The validation information can be secured against manipulation in any way.

For example, the validation information can be cryptographically signed using a private key of a public key cryptosystem. A keyed hash function can also be used, for example, whose calculation is also based on a secret key. Alternatively or in combination with this, for example the validation information and the target image can be mapped to a new target image by applying a steganographic function. A steganographic function encodes the validation information into the new target image in such a way that the change from the original target image is inconspicuous, at least from the perspective of a human viewer. In this way, a possible attacker is not even informed that the target image is secured with validation information.

• • auf einem Datenträger hinterlegt werden, dessen physikalischer Schreibprozess irreversibel ist; und/oder
• • auf einem externen Server oder in einer Cloud hinterlegt werden; und/oder
• • in einer Blockchain hinterlegt werden.

However, the validation information can also be stored at any desired location, for example separately from the target image, so that it is made more difficult for an attacker to manipulate both the target image and to reconcile the validation information with this manipulation. For example, the validation information

• • are stored on a data carrier whose physical writing process is irreversible; and or
• • be stored on an external server or in a cloud; and or
• • stored in a blockchain.

For example, optical storage media can be used as storage media whose physical writing process is irreversible, in which bits are written by local thermal degradation or destruction of a material in the storage medium. Likewise, for example, memory modules can be used in which local increased current flow causes local thermal degradation or destruction of an electrically conductive or semiconductive connection and bits can thus be written.

Storing the validation information on an external server or in a cloud creates the additional hurdle for an attacker not only to have to change the target image, but also to overcome the security barriers of the external server or cloud in order to have an opportunity for change to get the validation information.

Validation information once stored in a blockchain is in fact unchangeable afterwards, since the attacker would have to defeat the consensus mechanism of the blockchain used with a majority attack. Compared to irreversibly writable storage media, a blockchain has the advantage that the storage capacity is not limited from the outset by a physical medium as a finite resource.

If the validation information is stored separately from the target image, it can be tied to the specific target image even more securely, for example, by storing it in association with a hash value of the target image. Especially when using an irreversibly writable storage medium or a blockchain, this is considerably cheaper than storing the complete target image in addition to the validation information.

• • für den Aufruf der Bearbeitungsfunktion verwendbare Angaben, wie etwa eine Kennung der Bearbeitungsfunktion in einer Programmierschnittstelle (Application Programming Interface, API) oder eine Einsprungadresse, unter der die Bearbeitungsfunktion in einer Programmbibliothek oder im Arbeitsspeicher erreichbar ist, und/oder
• • einen Namen der Bearbeitungsfunktion, und/oder
• • einen Hashwert von Programmcode der Bearbeitungsfunktion

beinhalten.This identification of the processing function can in particular, for example

• • Information that can be used to call the processing function, such as an identifier of the processing function in an application programming interface (API) or an entry point at which the processing function can be reached in a program library or in main memory, and/or
• • a name of the editing function, and/or
• • a hash value of program code of the processing function

include.

In addition, the identification can, for example, also contain parameters with which the processing function was called up. This offers increased security against a processing function that is desired in image processing being “misused” to undesirably change or render unrecognizable semantic information in the image.

For example, the functions mentioned for adjusting brightness, contrast and other color corrections can be caused by setting extreme values of their parameters to process the source image into a target image in which practically all of the image information is at a top or bottom edge of the available range of pixel values is driven into saturation. In the target image, little or nothing of the semantic content of the source image can then be recognized. In an analogous manner, for example, the information that is actually of interest can also be erased from the images by using processing functions for removing noise from images.

A processing function that outputs a specific section of the source image as the target image could also be used by changing the coordinates of this section in order to generate a semantically different target image. For example, instead of a detail that shows a first object, a detail that shows another object or even just a background area without objects can be selected. The new target image would then have been created from the same source image and processed with the same editing functions as the previous target image, but it would have been substantially changed. It is therefore advantageous to include the coordinates of the selected image section in the identification of the processing function.

The selection of image sections is relevant, for example, in an advantageous embodiment of the method that optimally uses capacities for the further processing of target images.

In this embodiment, an image of an observed area recorded by a surveillance camera is selected as the source image. Several target images are generated that contain different sections of the source image. These target images are transmitted to one or more processing stations via a network and processed further by the one or more processing stations to produce processing results.

For example, several processing stations can be provided in the network, which process different target images in parallel. In this way, the combined capacity of these processing stations can be used to obtain the entirety of the work results obtained from the source image as quickly as possible. For example, sections of a large source image can be distributed more efficiently in a mesh network containing cameras and/or other sensor modules and processed on these modules. The cameras or sensor modules then do not have to be able to handle the required further processing for the entire large source image. For example, in the semantic analysis of images using neural networks and other image classifiers, the GPU memory requirement depends significantly on the image size. Especially on sensor modules and other IoT devices, the hardware equipment is often tightly dimensioned because on the one hand the hardware costs multiply with the number of devices used and on the other hand these devices are often supplied with energy from batteries.

The target images can also be transferred to a cloud, for example, where the capacity for further processing can be scaled automatically. For example, cloud services can be used to recognize objects or faces in images, which provide turnkey recognition “as a service”. When such a cloud service is presented with a large number of target images at once, that cloud service takes on the task of providing the capacity for these simultaneous requests. The same procedure can also be followed if further processing is implemented independently in a cloud.

For example, the logic of the processing station can be implemented in a software container. A central management instance (such as the event-driven "Lambda function" offered by Amazon AWS) can then receive the target images, distribute them to instances of the software container and scale the number of these instances up or down if necessary. For example, in a virtualized execution environment, a new worker instance of the software container can be created for each individual target image, which only processes this target image further and is then terminated again directly. Only as much capacity is then used and paid for as is actually required. Thus, for example, it is possible to react optimally to the fact that the need for further processing of target images can vary greatly even when monitoring the same area over and over again. For example, if the further processing is aimed at recognizing people in the target images by their faces, then the need for further processing depends on the number of people in the area. For example, the frequency of pedestrians is on a monitored space in public space varies greatly and is sometimes difficult to predict. Unexpectedly nice weather and higher temperatures, for example, can suddenly attract many people at once and create a great need for further processing, which then collapses again just as suddenly when the night-time curfew comes into force. On factory premises, for example, shift changes or the final completion of a larger machine can mean that suddenly many more people are on the move than at other times.

In a particularly advantageous embodiment, the target images can each contain at least one face, so that the one or more processing stations each supply an identification of at least one face (or the associated person) as the processing result.

This further processing can also, for example, work hand in hand with the previous processing of a source image to form a number of target images, such that sections that contain exactly one face are always selected. Then further processing can concentrate fully on this one face and does not first have to determine how many different faces the image contains.

In a further advantageous embodiment, the target images are used as source images after processing by the one or more processing stations, in order to aggregate a new target image from them. If validation information is also created for this target image, the decision-making basis for determining the collected processing results, for example, can be securely documented in the form of this target image.

In a further advantageous embodiment, several images recorded by different cameras carried by a vehicle are selected as source images. A target image composed of these source images is generated. A processing result is evaluated from the target image. The processing result is processed into a drive signal. The vehicle is controlled with this control signal.

In this way, the processing result can be determined, for example, starting from a single image that contains information from the entire vehicle environment or at least a significant part thereof. For example, for the further planning of the behavior of the vehicle, the complete traffic situation that is in front of the vehicle in the direction of travel is important. On the other hand, it is less important which of the various cameras observing this area captured which part of the traffic situation.

• • ein erster Teil des Objekts nur in einem ersten Quell-Bild und ein zweiter Teil des gleichen Objekts nur in einem zweiten Quell-Bild zu sehen ist; oder
• • das Objekt sich dort befindet, wo sich die in zwei Quell-Bildern erfassten Bereiche des Fahrzeugumfelds überlappen, so dass es in beiden Quell-Bildern zu sehen ist.

In a particularly advantageous embodiment, the processing result contains a classification of one or more objects contained in the target image and/or a semantic segmentation of the target image. For these tasks, it is particularly useful to work with a target image composed of several source images. In particular, then, for example, difficulties in recognition and multiple recognitions that can arise if

• • a first part of the object can only be seen in a first source image and a second part of the same object can only be seen in a second source image; or
• • the object is located where the areas of the vehicle's surroundings captured in two source images overlap, so that it can be seen in both source images.

As previously explained, the added value of the extended method for processing source images into target images is that the integrity of these target images can be checked at a later point in time. The invention therefore also provides a method for checking the integrity of a target image that was generated using the method described in the introduction.

As part of this process, the validation information stored in association with the target image is obtained. Furthermore, one or more candidate source images are obtained. These are the source images from which the target image was supposedly created. The information about which individual images are involved here can be stored in any form, which does not have to be particularly tamper-proof, in association with the target image. However, it is also possible, for example, to use all candidate source images from a predefined pool, such as all source images that were recorded in a specific period of interest.

A hash value of each candidate source image is determined by applying a predetermined hash function. In response to the fact that all of these hash values match the corresponding hash values in the validation information, it is determined that the target image has emerged from the candidate source image or from the candidate source images. In particular, it can also be checked, for example, whether there is also a candidate source image with a matching hash value for each hash value of an image in the validation information. If this is the case, none of the source images from which the target image was generated according to the validation information has been suppressed.

In a particularly advantageous embodiment, the processing function determined according to the identification in the validation information is also applied to the candidate source image or images, so that a test image is obtained. In response to the test image being consistent with the target image, it is determined that the target image is derived from the candidate source image(s) by applying the editing function according to FIG Identification has emerged in the validation information.

Here, the wording "consistent" means that the application of the processing function cannot only be successfully verified if the test image corresponds completely bit by bit to the target image. Rather, the comparison of the test image with the target image can allow a specified tolerance of deviations and/or be based on an evaluation criterion that is particularly meaningful with regard to the semantic match of the test image with the target image. For example, a two-dimensional correlation function can be used for the comparison. In this way, the comparison becomes resistant to rounding effects and other artifacts, among other things. Such effects can occur, for example, when the target image was created in a first hardware and software environment and the test image was created in a second, different hardware and software environment. For example, the program library with the editing function may have changed slightly between the creation of the target image and the creation of the test image as part of a possible update, so that a slightly different result is obtained after a call under the same identifier or entry address becomes.

The methods can be fully or partially computer-implemented and thus embodied in software. The invention thus also relates to a computer program with machine-readable instructions which, when executed on one or more computers, cause the computer or computers to carry out the method for creating an individual firmware. In this context, containers, compute instances and other execution environments on a virtualized platform in which machine-readable instructions can be executed are also to be regarded as computers.

The invention also relates to a machine-readable data carrier and/or a download product with the computer program. A downloadable product is a digital product that can be transmitted over a data network, i.e. can be downloaded by a user of the data network and that can be offered for sale in an online shop for immediate download, for example.

Furthermore, a computer can be equipped with the computer program, with the machine-readable data carrier or with the downloadable product.

character list

• 1: Ausführungsbeispiel des Verfahrens 100 zur Verarbeitung eines oder mehrerer Quell-Bilder 1 zu mindestens einem Ziel-Bild 3;
• 2: Beispielhafte Verarbeitung eines Quell-Bildes 1 zu mehreren Ziel-Bildern 3a-3c und zurück zu einem neuen Ziel-Bild 3' nach Gewinnung von Verarbeitungsergebnissen 7, 7a-7c;
• 3: Ausführungsbeispiel des Verfahrens 200 zur Prüfung der Integrität eines Ziel-Bildes 3.

The subject matter of the invention is explained below with reference to figures, without the subject matter of the invention being restricted thereby. It is shown:

• 1 : exemplary embodiment of the method 100 for processing one or more source images 1 to form at least one target image 3;
• 2 : Exemplary processing of a source image 1 into a plurality of target images 3a-3c and back to a new target image 3' after obtaining processing results 7, 7a-7c;
• 3 : Exemplary embodiment of the method 200 for checking the integrity of a target image 3.

1 is a schematic flowchart of an embodiment of the method 100 for processing one or more source images 1 to form at least one target image 3.

In step 110, the at least one target image 3 is generated by applying a predetermined processing function 2 to the source image or images 1.

In step 120, a hash value 1# of each source image 1 that contributed to the target image 3 is formed by applying a predetermined hash function 4.

• • für den Aufruf der Bearbeitungsfunktion 2 verwendbare Angaben, und/oder
• • einen Namen der Bearbeitungsfunktion 2), und/oder
• • einen Hashwert von Programmcode der Bearbeitungsfunktion 2

beinhalten.In step 130, validation information 5 for the origin of the target image 3 is determined from the hash value 1#, or the hash values, of the source image(s) 1 and an identification 2a of the processing function 2. According to block 131, the identification 2a of the processing function 2 can, for example

• • data that can be used to call processing function 2, and/or
• • a name of the editing function 2), and/or
• • a hash value of the program code of processing function 2

include.

In step 140 the validation information 5 is stored in association with the target image 3 . Here, according to block 141, the validation information 5 can be merged with the target image 3. For this purpose, for example, according to block 141a, the validation information 5 and the target image 3 can be mapped onto a new target image 3' by applying a steganographic function.

According to block 105, for example, an image of an observed area recorded by a surveillance camera can be selected as source image 1, for example. According to block 111, several target images 3, 3a-3c can then be generated, which contain different sections of the source image 1. In step 150, these target images 3, 3a-3c can then be transmitted via a network to one or more processing stations 6, 6a-6c, where in step 160 they are further processed into processing results 7, 7a-7c. Then, in step 170, the target images 3a-3c can again be used as source images 1 in order to aggregate a new target image 3′ therefrom analogously to step 110 described above.

According to block 106, several images recorded by different cameras 51-53 carried by a vehicle 50 can be selected as source images 1. According to block 112, a target image 3 composed of these source images 1 can then be generated. A processing result 7 can then be evaluated from this target image 3 according to block 161 . This processing result 7 can then be further processed in step 180 to form a control signal 8 , and in step 190 vehicle 50 can finally be controlled with this control signal 8 .

2 FIG. 12 illustrates an exemplary processing of a source image 1. Three sections, each containing a face, are selected from the source image 1 as target images 3a-3c. The validation information 5 obtained according to the method 100 certifies that each target image 3a-3c resulted from the original source image 1 by being cut out. In this case, this validation information 5 can optionally also specify the coordinates of the section selected from the source image 1 in each case.

The target images 3a-3c are each further processed in processing units 6, 6a-6c to produce processing results 7, 7a-7c, which in this example include an identification of the respective face. Subsequently, the target images 3a-3c are used as source images to form a new target image 3' by aggregation. This new target image 3' contains only the faces on the basis of which the processing results 7, 7a-7c were formed. If validation information 5 is formed again for this new target image 3', as described above, this can be used to certify that the target image 3' resulted from aggregation of the previous target images 3a-3c. Since these previous target images 3a-3c are in turn linked to the original source image 1 with validation information 5, it can ultimately be verified that the new target image 3' only emerged from the original source image 1 by cutting it out and then aggregating it .

3 FIG. 12 is a schematic flowchart of an embodiment of the method 200 for checking the integrity of a target image 3 that was generated using the method 100 described above.

In step 210, the validation information 5 stored in association with the target image 3 is obtained. This validation information 5 contains one or more hash values 1# of source images 1 and an identification 2a of a processing function 2 with which the target image 3 was generated from one or more source images 1.

In step 220, one or more candidate source images 1 are acquired.

In step 230, a hash value 1* of each candidate source image 1 is determined by using a predefined hash function 4.

In step 240 it is checked whether all hash values 1* match the corresponding hash values 1# in the validation information 5. If this is the case (truth value 1), it is determined in step 250 that the target image 3 has emerged from the candidate source image or images 1 .

In step 260, the processing function 2 determined according to the identification 2a in the validation information 5 can also be applied to the candidate source image or images 1, so that a test image (9) is created. In step 270 it can then be checked whether the test image 9 is consistent with the target image 3 . If this is the case (truth value 1), it is determined in step 280 that the target image 3 is derived from the candidate source image 1 or from the candidate source images 1 by using the processing function 2 according to the identification 2a in the validation information 5.

Reference List

1

Source image, candidate source image

1#

Hash value of the source image 1

1*

Hash value of a candidate source image 1

2

Editing function, leads from source image 1 to target image 3, 3a-3c

2a

Identification of the processing function 2

3, 3a-3c

target images

3'

new target image

4

hash function

5

Target image validation information 3, 3a-3c

6, 6a-6c

Processing stations, produce processing results 7, 7a-7c

7, 7a-7c

Processing results obtained from target images 3, 3a-3c

8th

control signal

9

test image

50

vehicle

51-53

Cameras carried with vehicle 50

100

Procedure for processing source images 1

105

Choosing a surveillance image as the source image 1

106

Selecting source images 1 from multiple cameras 51-53

110

Creation of the target image 3

111

Generation of several target images 3a-3c with different sections

112

Combining source image 1 to target image 3

120

Forming the hash value 1# for each source image 1

130

Find validation information 5

131

Choosing certain information for identification 2a

140

Depositing the validation information 5

141

Deposit by merging with the target image 3

141a

Fusing with steganographic function

150

Transmission of the target images 3, 3a-3c to stations 6, 6a-6c

160

Further processing of the target images 3, 3a-3c to results 7, 7a-7c

161

Further processing of a composite target image 3

170

Aggregating the target images 3 into a new target image 3'

180

Further processing of result 7 to control signal 8

190

Controlling the vehicle 50 with the control signal 8

200

Procedure for checking the integrity of the target image 3

210

Obtain validation information 5

220

Obtaining candidate source images 1

230

Determination of a hash value 1* per candidate source image 1

240

Check if hash values 1* and 1# match

250

Determine that target image 3 comes from candidate source images 1

260

Creation of test image 9 with editing function 2

270

Check that test image 9 is consistent with target image 3

280

Notice that Target Image 3 is created with Editing Tool 2

QUOTES INCLUDED IN DESCRIPTION

This list of documents cited by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent Literature Cited

• DE 102018126533 A1 [0004]

Claims (16)

Method (100) for processing one or more source images (1) to form at least one target image (3), with the steps: • the at least one target image (3) is generated (110) by applying a predetermined processing function (2) to the source image or images (1); • by applying a predetermined hash function (4), a hash value (1#) of each source image (1) is formed (120) that contributed to the target image (3); • Validation information (5) for the origin of the target image (3) is determined from the hash value (1#) or the hash values of the source image or images (1) and an identification (2a) of the processing function (2). (130); • the validation information (5) is stored (140) in association with the target image (3). Method (100) according to claim 1 , wherein the validation information (5) is merged (141) with the target image (3). Method (100) according to claim 2 , wherein the validation information (5) and the target image (3) are mapped (141a) to a new target image (3') by applying a steganographic function. Method (100) according to any one of Claims 1 until 3 , whereby the validation information • is stored on a data carrier whose physical writing process is irreversible; and/or • stored on an external server or in a cloud; and/or • stored in a blockchain. Method (100) according to claim 4 , The validation information (5) being stored in association with a hash value of the target image (3). Method (100) according to any one of Claims 1 until 5 , wherein the identification (2a) of the processing function (2) • for calling the processing function (2) usable information, and / or • a name of the processing function (2), and / or • includes a hash value of program code of the processing function (2). (131). Method (100) according to any one of Claims 1 until 6 , where • an image of an observed area taken by a surveillance camera is selected as the source image (1) (105), • several target images (3, 3a-3c) are generated (111), which represent different sections of the source image (1) include, • these target images (3, 3a-3c) are transmitted (150) via a network to one or more processing stations (6, 6a-6c), and • the target images (3, 3a-3c ) are further processed (160) by the one or more processing stations (6, 6a-6c) to produce processing results (7, 7a-7c). procedure after claim 7 , wherein the target images (3a-3c) each contain at least one face and wherein the one or more processing stations (6, 6a-6c) each provide an identification of at least one face as a processing result (7, 7a-7c). Method (100) according to any one of Claims 7 until 8th , wherein the target images (3a-3c) are used (170) as source images (1) after processing by the one or more processing stations (6) in order to aggregate a new target image (3') therefrom. Method (100) according to any one of Claims 1 until 6 , where • a number of images (51-53) recorded by different cameras (51-53) carried by a vehicle (50) are selected as source images (1) (106), • a target image composed of these source images (1). (3) is generated (112), • a processing result (7) is evaluated (161) from the target image (3), • the processing result (7) is further processed to form a control signal (8) (180) and • the vehicle (50) is controlled (190) with this control signal (8). Method (100) according to claim 10 , wherein the processing result (7) contains a classification of one or more objects contained in the target image (3) and/or a semantic segmentation of the target image (3). Method (200) for checking the integrity of a target image (3), which with the method (100) according to one of Claims 1 until 11 was generated, with the steps: • the validation information (5) stored in association with the target image (3) is obtained (210); • one or more candidate source images (1) are obtained (220); • a hash value (1*) of each candidate source image (1) is determined (230) by using a predetermined hash function (4); • in response to all hash values (1*) matching corresponding hash values (1#) in the validation information (5). (240), it is determined (250) that the target image (3) has emerged from the candidate source image (1) or from the candidate source images (1). Method (200) according to claim 12 , whereby in addition • the processing function (2) determined according to the identification (2a) in the validation information (5) is applied to the candidate source image or images (1), so that a test image (9) is obtained (260 ); and • in response to the test image (9) matching (270) the target image (3), determining (280) that the target image (3) is from the candidate source image (1), or from the candidate source images (1), by using the processing function (2) according to the identification (2a) in the validation information (5). Computer program containing machine-readable instructions which, when executed on one or more computers, cause the computer or computers to carry out a method (100, 200) according to one of Claims 1 until 13 to execute. Machine-readable data carrier and/or download product with the computer program Claim 14 . One or more computers with the computer program after Claim 14 , and/or with the machine-readable data medium and/or download product claim 15 .

Images