MXPA06009114A - Watermark detection - Google Patents

Abstract

A detector (100) detects the presence of a watermark in an information signal. The information signal is correlated with an expected watermark (Wi) for each of a plurality of relative positions of the information signal with respect to the watermark to derive a set of correlation results (64). Part of the correlation results (64) are cross-correlated (82) with information (81) about an expected shape of a correlation peak in the results. This can improve the sensitivity of the detector (100). The cross-correlation result (84) is compared with a threshold at peak detection unit (85). The threshold used in this comparison (85) is set in an adaptive manner according to the expected shape. The information (81) about an expected shape of the correlation peak can be based on knowledge of processing operations that the information signal has undergone or expected to have undergone or from the shape of previous correlation results.

Description

DETECTION OF WATER MARKS Field of the Invention This invention relates to the detection of a watermark in an information signal. Background of the Invention Watermarking is a technique in which a mark of a certain type is added to an information signal. The information signal to which the watermark is added may represent a data file, a still image, video, audio or any other type of media content. The tag is inserted into the information signal before the information signal is distributed. The tag is usually added in a way that is imperceptible under normal conditions, so that it does not degrade the information signal, for example, a watermark added to an audio file should not be audible under normal listening conditions. However, the watermark must be robust enough to remain detectable even after the information signal has been subjected to normal processes during transmission, such as coding or compression, modulation and so on. Many watermarking schemes employ correlation as a detection technique, with a signal under test being correlated with a signal containing an EEF: 173750 known watermark. In these systems, the presence of a watermark is indicated by one or more peaks in the correlation results. The document nA Video atermaking System for Broadcast Monitoring, "Ton Kalker et al., Proceedings of the SPIE, Bellingham, Virginia vol 3657, January 25, 1999, pp. 103-112, describes a scheme for detecting the presence of a watermark in transmitted video content In this document, the height of the resulting correlation peaks is compared to a threshold to decide whether the audio / video content has a watermark or not.The threshold value is selected in such a way that the false positive probability (the probability of declaring a watermark present, when in fact the audio / video has no watermark) is adequately low.A typical threshold value is 5s (five times the standard deviation of the correlation results) In most applications watermarked content will undergo several processing operations between the point at which a watermark is inserted into the content and the point at which the presence of water is detected. the watermark A common example of content processing is lossy compression, such as MPEG encoding. Typically, the processing effects are to reduce the correlation peaks that would normally be expected to occur during the watermark detection process.

Thus, the performance of a watermark detection technique based on finding correlation peaks is considerably reduced when trying to detect watermarks in content that has undergone these processes. Summary of the Invention The present invention seeks to provide an improved way to detect a watermark in an information signal. Accordingly, a first aspect of the present invention provides a method for detecting a watermark in an information signal, comprising: deriving a set of correlation results by correlating the information signal with a watermark for each of a plurality of relative positions of the information signal with respect to the watermark and determining whether a watermark is present when comparing at least part of the set of correlation results with information about an expected form of a correlation peak In the results, by using information about an expected form of the correlation peak, the sensitivity of the detector can be improved, this is because the detector can 'look' for a peak in a particular way, instead of just relying on the occurrence of a point above a certain height, the ability to detect watermarks that are only weakly present in an article of Media storage also provides the option to allow the watermark to be inserted more weakly into the content, thus reducing its visibility under inspection by potential fraudulent parties, or reducing its perceptibility under normal observation conditions. The functionality described here can be implemented in software, hardware or in a combination thereof. Accordingly, another aspect of the invention provides software to carry out the method. It would be appreciated that the software can be installed on the host apparatus at any point during the life of the equipment. The software can be stored in an electronic memory device, hard disk, optical disk or other machine-readable storage medium. The software can be supplied as a computer program product in a machine-readable carrier or can be downloaded directly to the device via a network connection. Additional aspects of the invention provide a watermark detector for carrying out any of the method steps and an apparatus for displaying an information signal that responds to the output of the watermark detector. Although the described embodiment refers to processing of an image or video signal (including digital cinema content), it will be appreciated that the information signal may be data representing audio or any other type of media content. Brief Description of the Figures The embodiments of the present invention will now be described, by way of example only, with reference to the accompanying figures, in which: Figure 1 shows a known way to insert a watermark into a content article . Figure 2 shows a first arrangement for detecting the presence of a watermark in a content article. Figures 3 and 4 show tables of correlation results to be used in the detector and method. Figure 5 shows a graph of correlation results data. Figure 6 shows an example of stored form data, used in the provision of. Figure 2. Figure 7 shows a unit for storing shape data. Figure 8 shows a second arrangement for detecting the presence of a watermark in a content article. Figure 9 shows a graph illustrating the effect of basing detection on groups of correlation results. Figure 10 shows an apparatus for presenting content incorporating the watermark detector. Detailed Description of the Invention As a background, and to understand the -Invention, a process for inserting a watermark will be briefly described, with reference to figure 1. A watermark pattern w (K) is constructed using one or more basic watermark patterns w. When a data payload will be carried by the watermark, a number of basic watermark patterns are used. The watermark pattern w (K) is selected according to the payload - a multi-bit K code - to be inserted. The code is represented by selecting a number of the basic patterns w and by decentering them from each other by a particular distance and direction. The watermark pattern w (K) combined represents a noise pattern that can be added to the content. The watermark pattern w (K) has a size of M x M bits and is typically much smaller than the content article. Consequently, the pattern M x M is repeated (gridded) 14 in a larger pattern that matches the format of the content data. In the case of an image, the pattern w (K) is squared 14 in such a way that it matches the size of the image with which it will be combined. A content signal is received and stored in volatile memory 16. A measure of local activity? (X) in the content signal is derived at each pixel position. This provides a measure for additive noise visibility and is used to scale the watermark pattern w (K). This prevents the watermark from being noticeable in the content, such as areas of equal brightness in an image. A total scaling factor s is applied to the watermark in the multiplier 22 and this determines the overall strength of the watermark. The choice of s is a compromise between the degree of robustness. what is required and the requirement of how perceptible the watermark should be. Finally, the watermark signal (K) is added 24 to the content signal. The resulting signal, with the watermark inserted in it, will then be subjected to several processing steps as part of the normal distribution of that content. Figure 2 shows a schematic diagram of a watermark detector 100. The watermark detector receives content that may have watermarks. In the following description it is assumed that the content is images or video content. The detection of watermarks can be carried out for individual tables or for groups of tables. The accumulated frames are divided into blocks of size M (for example, M = 128) and then bent into a volatile memory of size M x M. These initial stages are shown as block 50. The data in the volatile memory is then submitted to a Fast Fourier Transformation 52.

The next stage in the detection process determines the presence of watermarks in the data contained in the volatile memory. To detect whether the volatile memory includes a particular watermark pattern or not, the contents of the volatile memory and the expected watermark pattern are correlated. Since the content data may include several watermark patterns, a number of parallel branches 60, 61, 62 are shown, each carrying out the correlation with one of the basic watermark patterns WO, Wl, W2. One of the branches is shown in more detail. The correlation values for all possible displacement vectors of a basic Wi pattern are calculated simultaneously. The basic watermark pattern Wi (i = 0, 1, 2) undergoes a Fast Fourier Transformation (FFT) before its correlation with the data signal. The set of correlation values is then subjected to a Fast Fourier Transformation 63. Complete details of the correlation operation are described in US 6,505,223 Bl. The Fourier coefficients used in the correlation are complex numbers, with a real part and an imaginary part, representing a magnitude and a phase. It has been found that the reliability of the detector is improved significantly if the information of magnitude is discarded and only the phase is considered. A magnitude normalization operation can be carried out after the multiplication by points and before the inverse Fourier Transformation 63. The operation of the normalization circuit comprises dividing by points each coefficient between its magnitude. This global detection technique is known as Equalized Filtering Only of Symmetric Phases (SPOMF). The set of correlation results. from the previous processing is stored in a volatile memory 64. A small exemplary set of correlation results is shown in Figure 3. The content with watermarks is indicated by the presence of spikes in the correlation results data. The shape of the peak can be better understood by viewing the correlation results in the form of a graph, with the correlation value being plotted as height over a baseline of the graph, as shown in Figure 5. The set of correlation results is examined to identify peaks that could be due to the presence of a watermark in the Content data. The presence of a watermark can be indicated by a pronounced and isolated peak of significant height, although the more isolated peaks tend to represent spurious coincidences due to noise.

It is more likely that prior processing operations during the distribution of the content caused a correlation peak due to a watermark to have been spotted or marked on several adjacent positions in the correlation results. An initial processing step 65 identifies candidate groups of correlation results data that could represent correlation peaks. A technique for identifying candidate peaks is described in more detail below. Once the candidate peaks have been identified, they are each tested to determine which represents a correlation peak that is due to a = water mark. The correlation results in a group are cross-correlated 82 with data 81 from a storage 80, representing an expected peak shape. The result of this cross-correlation gives an indication of the similarity between the shape of the data stored in the volatile memory 64 and the expected form. The result of the cross-correlation is compared to a threshold in the peak detection unit 85. The threshold used in this comparison 85 is not a constant value, but is established in an adaptive manner according to the expected form. The threshold depends on the sum of. squares of the expected peak height, which could be called the energy of the expected peak shape. This has the effect of normalizing the result of the cross-correlation. This stage reduces the occurrence of false matches between the actual group of results and the expected form of the results only because the expected form has high energy. Effectively, this requires that the expected peak shape be single energy. The stored form data may also be used as part of the candidate search stage 65. For example, knowing that a relatively flat shape is expected, the candidate search stage 65 may lower the threshold used to select candidate groups of such a kind. so that low peaks in the correlation results are not excluded. There are several ways in which stored form data can be collected. The form data can be provided as a file that accompanies the detector 100 and which is installed together with the detector. Updates can be provided on a periodic basis. Alternatively, or in addition to using an initial data set, it is possible for the detector to acquire shape data based on the correlation results that it observes, in use. A table of data of form can be stored, the table being arranged according to: processes that a signal of content has suffered during its distribution, type of signal of content, or the type of distribution channel. Each type of processing that a content signal suffers during its distribution will have an effect on the data in that signal, and this will affect the shape of the correlation peak when the detector 100 verifies the presence of a watermark. The effect of each process can be observed and stored as shape information in unit 80. When it is possible to quantify which processes a content signal has suffered during its distribution, it is possible to apply an appropriate form in the cross-correlation stage 82 of the detector. When a signal has suffered several. processes (eg, MPEG encoding and encoding for transmission over a wireless channel) various form data may be combined, or a suitable template corresponding to a particular process combination may be retrieved. Templates can be stored for a range of commonly used content types or distribution methods, for example, MPEG video received over a transmission channel; MP3 audio content received "via a wired connection; content received via a wireless connection." Information about the type or distribution of content is provided as input 40 to unit 80, the information 40 being obtained from another part of the receiver. Templates can be provided for different bit rates of content, for example, MPEG 2Mbps, 4Mbps, 6Mbps, etc., conversion of formats, for example, PAL in NTSC, NTSC in PAL, and also MPEG conversion and format combinations. This data table would be determined by the manufacturer of the watermark detector, and the relevant settings programmed into the detector at its installation. The templates can be changed by updates to the detector. The shape data comprises a set of numerical values that together define the shape of an expected peak. The form originates from the relative size of the numerical values in the set. The set of values can be scaled to any size. Thus, it is the shape of the peak rather than its size that is compared in the cross-correlation stage 82. Figure 6 shows an example of the type of information table so that it can be stored by the unit 80. Each type of content, process or combination of processes 102 is associated with form data 103 and a detection threshold 104 to be used by unit 85. Although shape data 103 are shown here in graphic form, they will in fact comprise a set of numerical values which they define together an expected peak shape. The use of data stored in this way may not be possible when, for example, the detector does not receive information about what processes the content has suffered or when the receiving equipment itself does not know this information. In this case, several techniques can be used to calculate the expected peak shape. Figure 7 shows a modality in which an average movement of form data is acquired over a period of time. New peak shape information 83 from the volatile memory of correlation results (or candidate search unit 65) is sent to an averaging function 91. Previous data, such as previous run average, are retrieved 92 of the stored data 90, a new average is calculated, and the updated average is returned 93 for storage. The average movement can be calculated on the previous detections D. The value of D is application dependent and will depend on the number of detections carried out per second in relation to the period of time during which the content / processing remains constant. This approach can be particularly useful when the processing applied to the content remains constant during several detection periods. When information about the type of content, or distribution processes or channel for the content is known, a plurality of stored templates can be acquired over a period of time, each associated with those processes or channels. Referring again to Figure 7, the unit 80 also includes a suitable interface 95 that receives information 40 and appropriately removes the data and store threshold 90. The form data 81 is sent to the cross correlator 82 and the data of decision threshold 86 are sent to the peaks detection unit 85. Figure 8 shows a further development of the invention. Each branch 60, 61, 62 of the detector 100 includes the features that are shown in detail in the branch 60. The unit 80 acquires shape data from the volatile memories 64 of each branch 60, 61, 62 and combines the data to derive a full form template. The combined data and the decision threshold data can then be applied to the correlation units 82 in each of the branches 60, 61, 62. A simplified mathematical example of the shape matching process will now be described. Consider that a content article has been correlated with a watermark pattern of interest using the SPOMF technique previously described and the correlation results stored in the volatile memory 64. The correlation results in the volatile memory 64 are a vector and correlation values with each element corresponding to a (cyclic) shift different from the watermark pattern in relation to the content signal. For reasons of clarity, it is assumed that y is one-dimensional although it can be appreciated that for most of the content the correlation results in the volatile memory 64 will be a bidimensional matrix corresponding to displacements in the horizontal and vertical directions. In the case of material without watermarks (f) it has been shown that the elements of y are approximately Gaussian White Noise (WGN). In the case of the material with watermarks (ff), the experiment shows that the results of the volatile memory are again approximately Gaussian noise, but there is also a peak. Suppose that the shape of the correlation peak, for a payload displacement, can be described by: r St (k) = A? Aid (k-t -i) (1)? = 0 This is a very general model of the correlation peak that considers its degree as being C adjacent positions in the volatile memory, with its form determined by: and its height that will be given by the scale factor A. The form ? ~ of known (expected) peak a is cross-correlated with the contents of the volatile memory and, and then compared with a threshold to decide whether the watermark is presentv- ^ w or not. The calculation of the payload displacement is taken as the position that maximizes the cross-correlation.

The derivation of this detection criterion is provided in the appendix. As a simple example of the benefit of using peak information, consider the case in which the peak shape is known to be flat, that is:, = a, Vie. { Or ... C-l} Figure 9 shows the minimum required average height of the volatile memory results and ± in the position corresponding to the watermark peak for the watermark to be declared present. These have been calculated in such a way that the same false positive probability is achieved as an existing detection method with a simple threshold of 5s. It can be seen that for widely scattered beak forms, ie, large groups of points C, the watermark can be successfully detected at peak heights much lower than the 5s level required by the current detectors. A process for identifying candidate correlation peaks in the correlation results will now be described for use in unit 65 of FIGS. 2 and 8. The grouping algorithm forms a number of groups of points, any of which may correspond to the peak of true correlation. The probabilities of these groups are compared, and the group with the lowest probability is assumed to be the desired correlation peak. The algorithm comprises the following steps: 1. Establish a threshold value and find all the points in the correlation data that are above this threshold value. All points that satisfy this criterion are stored in a list -ptsAJooveThresh. A suggested threshold value is 3.3s (s = standard deviation of results in volatile memory) although this can be set to any preferred value. A preferred scale is 2.5 - 4s. If the threshold value is set too low under a large number of points, which does not correspond to the presence of a watermark, it will be stored in the list. Conversely, if the value is set too high there is a risk that the points corresponding to a valid, but spotted, peak will not be added to the list. 2. Find the point with the highest absolute value. ... 3T._ Form candidate groups, that is, groups of correlation points. . Candidate groups are formed by collecting points that not only have 'significant' value (a value greater than the threshold), but also are located very close to at least some other point of significant value. This is accomplished as follows: (i) The first point of the ptsAboveThresh list is removed and entered as the first p point of a new group; (ii) Points in ptsAboveThresh that are within a distance d from point p are searched. All of these points are removed from the ptsAboveThresh list, and they are added to the group; (iii) The next point in the group is taken as the current p point. Step (ii) is repeated to add to the group all points in ptsAboveThresh that are within the distance d of the new point p. (iv) Stage (iii) is repeated until ptsAboveThresh has been processed for all points in the group; (v) If the resulting group consists of a single point and that point is not equal to the highest peak found in stage 2 above, then this group is discarded; (vi) Steps (i) to (v) are repeated until ptsAboveThresh is empty. At the end of this procedure, all points originally entered in ptsAboveThresh in stage 1 above have been either: - assigned to a group which contains other points in the ptsAboveThresh list that are close to it, or - discarded, since they do not have neighbors of similar height, and therefore are not part of a group. A group is only allowed to understand a single point if that point has the greatest absolute height of all points in the volatile correlation memory. This prevents a pronounced, non-stained correlation peak from being discarded, but prevents other isolated peaks, representing real noise, from being used. Referring again to Figures 3 and 4, these show some exemplary sets of correlation data of the type that could be calculated by the detector. Figure 3 shows a set of results for a spotted peak, with values varying between -3.8172 and 4.9190. Watermarks can be inserted with negative amplitude, giving a peak of negative correlation. The highest value of 4.9190 is shown within the box 130. Although this is below the typical detection threshold of 5, the highest value is surrounded by other correlation values of a similar value. This is an indicator of a peak that has been stained by processing during the distribution chain. Following the procedure described above, and establishing a threshold T of 3.3 and a distance of 1, it can be found that the correlation values within the ring 140 satisfy these criteria. Working through the process, the results of significant value are all located along each other. When viewing the data shown in Figure 4, the values vary between -3.7368 and 10.7652. Applying the same detection criteria, only one point 160 exceeds the threshold. The value of this point clearly exceeds the threshold and is thus considered to be a valid peak. From inspecting the adjacent values, it can be seen that this represents a pronounced correlation peak. The inserted information represented as payload code K can identify, for example, the owner of copyrights or a description of the content. In the protection of DVD copies, it allows the material to be labeled as a single copy, "so that it is never copied", "without restrictions", "no more copies", etc. Figure 10 shows an apparatus for recovering and presenting a content signal that is stored in a storage medium 200, such as an optical disk, memory device or hard disk. The content signal is removed by a content removal unit 201. The content signal 202 is applied to a processing unit 205, which decodes the data and validates it for display 211, 213. The content signal 202 also it is applied to a watermark detection unit 220 of the type previously described. The processing unit 205 is arranged in such a way that it is only allowed to process the content signal if a predetermined watermark is detected in the signal. A control signal 225 sent from the watermark detection unit 220 informs the processing unit 205 whether the processing of the content should be allowed or denied, or informs the processing unit 205 of any copying restriction associated with the content . Alternatively, the processing unit 205 may be arranged in such a way that it is only allowed to process the content signal if a predetermined watermark is not detected in the signal. In the above description, a set of three watermarks has been considered. However, it will be appreciated that the technique can be applied to find a peak of correlation in content data carrying only a single watermark, or to content data carrying any number of various watermarks. In the foregoing description, and with reference to the figures, a detector 100 is described which detects the presence of a watermark in an information signal. The information signal is correlated with an expected watermark Wi for each of a plurality of relative positions of the information signal with respect to the watermark to derive a set of correlation results. 64. Part of the correlation results 64 cross-correlate 82 with information 81 about an expected form of a correlation peak in the results. This can improve the sensitivity of the detector 100. The result of the cross-correlation 84 is compared to a threshold in the peak detection unit 85. The threshold used in this comparison 85 is established in an adaptive manner according to the expected form. The information 81 about an expected form of the correlation peak can be based on the knowledge of processing operations that the information signal has suffered or is expected to have suffered, or based on the form of the previous correlation results.

Appendix This section derives the exemplary detection algorithm given above, and describes how to set the detection threshold to achieve a desired false-positive probability. Suppose that for content with watermark -. the correlation results are a peak due to the watermark, plus WGN. This is supported by the observation that, with the exception of the peak itself, in the case of content with watermark the correlation results are again distributed approximately Gaussian. The following hypothesis test can then be written to detect the presence of a watermark: (Hw ~) y = n wherein n is a vector of length N of independent WGN values and St is a vector of length N which corresponds to the shape of the watermark correlation peak, cyclically displaced by positions t within the volatile correlation memory. In the following work it is assumed that the noise has a standard unit deviation. This is achieved by normalizing the correlation results before the detection of watermarks. Suppose momentarily that both the peak form s and the payload displacement t are known, the PDFs under each hypothesis are the following. Under \ ™ -w) the values in y are pure WGN with PDF: Under Hw the volatile memory contains a peak plus WGN and has PDF: A decision between the two hypotheses will be made using a probability relation test: Probability (y \ s, t) where the log-likelihood relationship is: The following model of the watermark correlation peak St is assumed: ci 5T (&) = A? «, (&-ti) ¡= o (6) This describes a peak that spans C points, which is shaped known, given by a, but an unknown total height, given by the scale factor A. It is assumed that C is known. In practice, a calculated value would have to be used based on the typical degree of spreading of watermark correlation points, or a value of C can be obtained using the group detection technique described above. Substitute equation 6 in the log-likelihood expression of. Equation 5 gives: The unknown parameters. { A, t) will be calculated by the values that maximize the probability of the observed data (y). Maximize with respect to the unknown peak height gives: and the log-probability becomes: Select the calculation of the load displacement f useful to maximize the probability da; Note that the sum in the denominator is a constant that has no dependence on the correlation results in y. The probability ratio decision rule is therefore reduced to a threshold test on the magnitude of the cross-correlation between y and the peak form a: where it is selected as the displacement that maximizes t the cross correlation. The necessary threshold value h to achieve an acceptably low false positive probability of the value is given by: C-1 Vx [False positive ^ Pr? A, and t +? > h H "= ¡= 0 Under the Hw hypothesis, the elements are independently distributed in Gaussian form with zero mean and unit deviations. The variable?, Defined as: C-1? (K) =? A¡y (k + i)? = 0 therefore also has a Gaussian distribution but no standard deviation c-i AS a. í = 0 Using this annotation, equation 8 becomes: 7 (k) < -h, Vk] + Vr [? (k) > + h ^ k] = a = > 2 [l- (Pv [? < h]) N = of which the appropriate value of h can be determined by means of tables of F (a) = Pr (Z <a), where Z is a random Gaussian variable of zero standard and unit deviation. The dependence of the detection threshold in s and provides the adjustment according to the energy of the given peak shape, in such a way that the desired false-positive probability is obtained. It is noted that in relation to this date, the best method known to the applicant to carry out the aforementioned invention, is that which is clear from the present description of the invention.

Claims (15)

2. Having described the invention as above, the content of the following claims is claimed as property: 1. A method for detecting a watermark in an information signal, characterized in that it comprises: deriving a set of correlation results by correlating the signal of information with a watermark for each of a plurality of relative positions of the information signal with respect to the watermark and determining whether a watermark is present when comparing at least part of the set of correlation results with information about in a way expected from a correlation peak in the results. The method according to claim 1, characterized in that the comparison comprises a cross-correlation of at least part of the set of correlation results with information about the expected form of a correlation peak.
3. 3. The method according to claim 1 or claim 2, characterized in that it further comprises comparing the comparison output with a threshold value to determine the presence of a valid watermark.
4. 4. The method according to claim 3, characterized in that the threshold value varies according to the expected form of the correlation peak.
5. The method according to any of the preceding claims, characterized in that the information about an expected form of the correlation peak is derived from the knowledge of processing operations that the information signal has suffered or is expected to have suffered.
6. The method according to any of the preceding claims, characterized in that the information about an expected form of the correlation peak is derived from the form of previous correlation results.
7. The method according to claim 6, characterized in that the previous correlation results are results for: the same type of information signal; an information signal that has been subjected to the same processing stages; an information signal that has been distributed through the same channel.
8. The method according to any of the preceding claims, characterized in that it further comprises identifying groups of correlation results that are likely to represent peaks of correlation, and carrying out the step of determining whether a watermark is present only in the groups of results identified.
9. 9. The method according to claim 8, characterized in that the step of identifying groups of correlation results comprises determining all the correlation results in the set that exceed the threshold value and then determining which of those correlation results are located within a distance. predetermined from each other.
10. The method according to any of the preceding claims, characterized in that a plurality of watermarks are used, the step of deriving a set of correlation results being repeated for each watermark, the method further comprising determining information about the shape of a correlation peak in the correlation results for one of the watermarks, and use that information in a comparison for another of the watermarks.
11. 11. Software characterized in that it is for carrying out the method according to any of the preceding claims.
12. A watermark detector for detecting a watermark in an information signal, characterized in that it comprises: means for deriving a set of correlation results by correlating the information signal with a watermark for each of a plurality of relative positions of the information signal with respect to the watermark and means for determining whether a watermark is present by comparing at least part of the set of correlation results with information about an expected form of a correlation peak in the results .
13. 13. The watermark detector according to claim 12, characterized in that it further comprises means for carrying out any of the steps of the method according to claims 2-10.
14. The watermark detector according to claim 12 or 13, characterized in that the means for deriving a set of correlation results and the means for determining whether a watermark is present comprise a processor that is arranged to execute software to carry out those functions.
15. 15. Apparatus for presenting an information signal comprising means for disabling the. operation of the apparatus depending on the presence of a valid watermark in the information signal, characterized in that it comprises the watermark detector according to any of claims 12-14.