WO2016045977A1 - Procede et appareil pour la determination de l'incorporation ou non d'un symbole de tatouage numerique specifique parmi un ou plusieurs symboles de tatouage numerique candidats dans une section courante d'un signal audio recu - Google Patents

Procede et appareil pour la determination de l'incorporation ou non d'un symbole de tatouage numerique specifique parmi un ou plusieurs symboles de tatouage numerique candidats dans une section courante d'un signal audio recu Download PDF

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WO2016045977A1
WO2016045977A1 PCT/EP2015/070685 EP2015070685W WO2016045977A1 WO 2016045977 A1 WO2016045977 A1 WO 2016045977A1 EP 2015070685 W EP2015070685 W EP 2015070685W WO 2016045977 A1 WO2016045977 A1 WO 2016045977A1
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values
value
candidate
watermark
pdf
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PCT/EP2015/070685
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Michael Arnold
Peter Georg Baum
Xiaoming Chen
Ulrich Gries
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Thomson Licensing
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/018—Audio watermarking, i.e. embedding inaudible data in the audio signal

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  • the invention relates to a method and to an apparatus for determining from sets of correlation result values whether a specific watermark symbol out of one or more candidate wa ⁇ termark symbols is embedded in a current section of a re ⁇ ceived audio signal, or whether no watermark symbol is em ⁇ bedded in the current section of the received audio signal.
  • a watermark detector In a watermark detector cross correlations between a received signal and reference patterns are evaluated. Basical- ly, the maximal correlation result value is compared to a threshold in order to determine whether watermark information has been embedded in the received signal. For acous ⁇ tic path transmission, multiple correlation result peaks are employed for detection, in order to take a multi-path envi- ronment into account. Again, an appropriately defined metric aggregating multiple correlation result peaks is compared to a threshold for watermark detection.
  • a false positive probability defines the probability that a watermark is detected for unmarked content and is denoted as Pf V , which is naturally dependent on the applied watermark detection processing.
  • a problem to be solved by the invention is to provide an im ⁇ proved watermark information detection. This problem is solved by the method disclosed in claim 1. An apparatus that utilises this method is disclosed in claim 2.
  • order statistics are used for watermark symbol detection from the correlation result values, where the joint probability distribution function (pdf) for one or more peaks of cross correlation values between a current section of the received audio signal and reference patterns is employed directly for watermark detection.
  • PDF joint probability distribution function
  • Monte Carlo or quasi-Monte Carlo simulations are used for evaluating the false positive probability corre ⁇ sponding to a pdf value threshold.
  • a pdf threshold look-up table (LUT) and an associated false positive probability look-up table can be constructed, which both are used for the watermark symbol detection.
  • the derived false positive probability has intuitive interpretation, it can be used for the design of watermarking systems employing correlation for watermark detection.
  • the inventive method is adapted for determin- ing from sets of correlation result values whether a specif ⁇ ic watermark symbol out of one or more candidate watermark symbols is embedded in a current section of a received audio signal, or whether no one of said candidate watermark sym ⁇ bols is embedded in said current section of said received audio signal, wherein said current section of said received audio signal was correlated with at least one candidate ref ⁇ erence pattern, each one of which representing one of said one or more candidate watermark symbols, said method includ- ing :
  • the inventive apparatus is adapted for deter- mining from sets of correlation result values whether a specific watermark symbol out of one or more candidate water ⁇ mark symbols is embedded in a current section of a received audio signal, or whether no one of said candidate watermark symbols is embedded in said current section of said received audio signal, wherein said current section of said received audio signal was correlated with at least one candidate ref ⁇ erence pattern, each one of which representing one of said one or more candidate watermark symbols, said apparatus in ⁇ cluding means configured to:
  • a) take from the current set of correlation result values a group of maximal values which together form a peak vector; obtain from the values of said peak vector a value of a probability distribution function;
  • false positive probability value represents a probability that peaks re ⁇ sulting from correlation between a candidate reference pattern and non-watermarked audio signal content have a smaller pdf value than said probability distribution function value; - determine whether said false positive probability value is smaller than a first threshold value and, if true, deter ⁇ mine that the current candidate watermark symbol is the wa ⁇ termark symbol present in said current section of said re ⁇ ceived audio signal;
  • Fig. 5 first flow diagram for watermark detection based on order statistics
  • FIG. 6 block diagram for watermark information detection in a received audio signal
  • Fig. 7 second flow diagram for watermark detection based on order statistics.
  • a watermark detector In a watermark detector, cross correlations between a received signal and reference patterns are evaluated. Usually, the maximal correlation result value is compared to a threshold in order to determine whether a watermark is em- bedded in the received signal.
  • multiple correlation result value peaks are employed for watermark detection, in order to take a resulting multi-path environment due to echoes and rever- beration into account.
  • An appropriately defined metric ag ⁇ gregating multiple correlation result value peaks is com ⁇ pared to a threshold for watermark detection.
  • a false posi ⁇ tive probability defines the probability that a watermark is detected for unmarked content and is denoted as Pf p . It is naturally dependent on the applied detection method.
  • Pf p itself may be used for watermark detection.
  • Pf p values are evaluated for different symbols in the watermark symbol al ⁇ phabet.
  • the smallest Pf p among all watermark symbols is com ⁇ pared to a threshold in order to decide whether watermark information is present in the received signal. If the small ⁇ est Pf p is smaller than the threshold, a watermark is assumed to be present.
  • the symbol associated with the smallest Pf p is taken as the embedded watermark symbol. Otherwise, if the smallest Pf p is higher than the threshold, it is declared that no watermark data is present.
  • P fp is defined as the probability that n p or more correlation result values for a random correlation array subject to Gaussian distribution are larger than or equal to the actual n p peaks under consideration.
  • detection is based on comparison of multiple peaks.
  • the number of disjoint comple- mentary cases exponentially increases with increased n p , which limits its application, especially for environments with severe reflections and/or reverberations.
  • n p > 1 that is not the case, as illustrated in Fig. 1, in which Pf p values delivered from the detector de- scribed in WO 2011/141292 Al and PCT/EP2014/066063 are com ⁇ pared to Pf p thresholds shown on the x-axis.
  • the probability for Pf p values being lower than a threshold is estimated by dividing the number of Pf p values lower than the threshold by the total number of delivered Pf p values.
  • the probability of evaluated Pf p values for unmarked content watermark being smaller than the given Pf p threshold is high- er than the given Pf p threshold for n p > 1. With increased n p the deviation between both becomes larger.
  • order statistics are used for watermark detection.
  • two look-up tables are employed for Pf p func ⁇ tion evaluation.
  • order statistics as decision metric provides a nice interpretation of evaluated Pf p function values, namely, the probability of evaluated Pf p function values for unmarked content watermark being smaller than the given Pf p threshold is exactly equal to the given Pf p threshold for any n p value.
  • the probability distribution function denoted pdf of peaks re ⁇ sulting from unmarked content can be employed for watermark detection.
  • the decision criterion is to minimise the likelihood pdf. That is, the higher the pdf value for multiple peaks, the more likely it is that these peaks are generated from unmarked content. Conversely, the lower the pdf value, the more likely it is that these peaks are generated from marked content.
  • the pdf of multiple peaks occurring in the correlation re ⁇ sult can be evaluated based on order statistics, see H.A. David and H.N. Nagaraja, "Order statistics", John Wiley & Sons, 3rd edition, 2003.
  • the con- straint v 0 ⁇ v ⁇ ⁇ v rip _ 1 is referred to as peak constraint.
  • the joint pdf of these peaks can be derived as (see the above mentioned David/Nagaraj a book):
  • watermark detection can be carried out by com- paring the pdf values of normalised peak vectors in correla ⁇ tion arrays corresponding to different watermark symbols, and the symbol resulting in the smallest pdf value is se ⁇ lected as embedded watermark symbol.
  • a threshold should be used to avoid a high false positive probability, or in other words, the resulting Pf p using that threshold should be below the target Pf p . That is, only when the smallest pdf value g(yv) is sufficiently low, it is decided that a watermark is present in the received signal. Otherwise, if the threshold is not low enough, for unmarked content, a watermark will be detected with a high probability. Consequently, the corresponding Pf p becomes high. Therefore it is necessary to evaluate Pf p for a specific threshold for pdf values g(yv) . Evaluation of false positive probabilities based on Monte Carlo simulation
  • the pdf values g(w) for different watermark symbols are compared to a threshold in order to decide whether or not a watermark is present. If g(w) is smaller than the threshold, it is decided that a watermark is present. And the watermark symbol resulting in the smallest pdf value is taken as the embedded one. If none of evaluated pdf values is smaller than the threshold, it is assumed that no watermark information data is present.
  • gz is interpreted as a threshold for de- termining the presence of watermark.
  • a larger number of length-L correlation arrays can be generated according to the Gaussian distribution. Normalised peak vectors of these correlation arrays are denoted as (wW, 1 ⁇ i ⁇ M] and are used for evaluat- ing g(w ⁇ 1 ⁇ .
  • m denote the number of generated correlation arrays fulfilling g(v ⁇ ) ⁇ gz)
  • Pf P (z) lim M ⁇ 0O —.
  • the pdf values for extremely small or extremely large peak values are extremely small.
  • Pf p is represented by the area below the distribution function where pdf values are smaller than the threshold. Therefore, the evaluation of Pf p can be interpreted as one-dimensional integration for the single-peak case. For multi-peaks, it is a multi-dimensional integration.
  • Fig. 2 indicates that an increase of the threshold th also increases Pf p . Therefore in the above experiment g(w ⁇ ) ⁇ g z) also indicates that ⁇ ( ⁇ ') ⁇ Pf P (z) , where g z) and g(w ⁇ 1 ⁇ are interpreted as two thresholds. Consequently, if calculating Pf p for each correlation array, there are m values lower than Pf P (z) out of M calculated Pf p values, i.e.
  • the multi-dimensional point w * can be derived as follows:
  • the false positive probability is determined numerically.
  • the evaluation of the false positive probability can be interpreted as multi ⁇ dimensional integration.
  • the convergence of Monte Carlo or quasi-Monte Car ⁇ lo simulations is independent of dimension, while linear-grid based methods do depend on dimension and therefore do not converge well with increased dimension. Therefore the Monte Carlo simulation is used for the numerical evaluation of the false positive probability, whereby the Monte Carlo simulation is carried out according to the Monte Carlo method.
  • LUTs used for watermark detection.
  • One LUT stores values of probability distribution function (pdf) for normalised peaks of correlation between non-watermarked content and reference patterns, and the other one stores values of false positive probability corresponding to entries in the pdf LUT. That is, each entry in the pdf LUT corresponds to a unique entry in the LUT for false positive probability. Dif ⁇ ferent correlation lengths and different number of peaks re ⁇ sult in different LUTs. And determined LUTs are stored in the memory unit of watermark detector, which is accessed during watermark detection.
  • PDF probability distribution function
  • the detector performs correlation between received audio section and ref ⁇ erence patterns corresponding to watermark symbols. Correla ⁇ tion values are sorted to find peaks, which are normalised by standard deviation. The standard deviation is estimated either individually for each set of correlation result val ⁇ ues corresponding to individual candidate watermark symbol, or by averaging over sets of correlation result values. Afterwards, the probability distribution function is evaluated for the normalised peaks. And the LUT for probability dis ⁇ tribution is accessed to find the entry index which is near ⁇ est to the evaluated pdf value from the normalised peaks. This entry index is then used to access the second LUT for the false positive probability. And the false positive prob- ability corresponding to the peaks found is then evaluated by means of interpolation or extrapolation.
  • n p -dimensional hypercube D [w rnin , w max n v is used for Monte Carlo simulation.
  • all volume outside the hypercube is ignored for the Pf p evalua ⁇ tion.
  • the inventors have found that, by a careful choice of w min , w max , the influence on the evaluated P p values is negli ⁇ gible for relevant Pf p values in practical applications.
  • w max > w min is
  • Fig. 3 depicts the determination of w min ,w max for the single- peak case.
  • the false positive probability can be reformulated as (see the definition in equation (2)):
  • equation (3) is the expecta ⁇ tion of g'(w) with respect to a uniformly distributed random vector w :
  • n p random variables [w 0 ,w 1 , ... ,w rip _ 1 ] uniformly distrib- uted in D are generated M times.
  • the pdf value ⁇ (w) is evaluated and compared to the pdf threshold th. If ⁇ (w) ⁇ th, g(yv) values are accumulated.
  • the final result of accumula- tion is scaled by — , which delivers an estimated false pos- itive probability. Consequently, for each entry in the pdf threshold LUT, the corresponding false positive probability is determined numerically according to equation (4) .
  • Another LUT for Pf p is constructed. Based on interpola ⁇ tion/extrapolation, mapping from pdf values to false positive probabilities is established. More specifically, given a normalised peak vector w, the corresponding pdf ⁇ (w) is evaluated according to equation (1) . The entry in the pdf threshold LUT is found which is nearest to ⁇ (w) . If the en ⁇ try is not at the boundary of the pdf threshold LUT, the corresponding entry in the Pf p LUT and its neighbors are used to evaluate the false positive probability corresponding to g(w) by means of interpolation. If the entry in the pdf threshold LUT nearest to g(w) is at the threshold LUT bound- ary, extrapolation may be necessary to calculate the corre ⁇ sponding false positive probability for g(yv) .
  • Fig. 4 shows a flow diagram for the generation of the pdf threshold LUT and Pf p LUT, which are used for the watermark detection .
  • step 41 the aim is stated to construct a pdf threshold LUT with K entries, given a pdf range [p min ,p max ] .
  • Normalised peak vectors are generated in step 43: Generate M times normalised peak vec ⁇ tors using Monte Carlo or quasi-Monte Carlo processing.
  • Monte Carlo a random generator is used to generate normalised peak vectors uniformly distributed in the hyper- cube [w min ,w max ] np , where [w min ,w max ] defines the range of generated random normalised peak values.
  • a low-discrepancy sequence like Sobol sequence is generated as normalised peak values, which also approxi ⁇ mate the uniform distribution.
  • each generated normalised peak vector is sorted such that the peak constraint is fulfilled: w 0 > w ⁇ ⁇ w Up _ lr which is used to calculate pdf value ⁇ (w ⁇ ) in step 45.
  • step 46 the calculated values ⁇ (w ⁇ ) are compared with threshold entries t/ij in the pdf threshold LUT. If g(w ⁇ m ⁇ th ir the Pf p LUT entries are updated in step 47: all Pf V entries in the Pf p LUT having a corresponding pdf threshold greater than are increased by g(w ⁇ m ⁇ . Thereafter i is incre ⁇ mented and, as long as i ⁇ K in step 48, the i loop continues with step 46.
  • step 49 Thereafter m is incremented and, as long as m ⁇ M in step 49, the m loop continues with step 43.
  • step 43 After generating all M times normalised peak vectors and corresponding updating of the Pf p LUT entries, the final Pf p values are estimated in step y scaling the (y v max ⁇ w min ) ⁇
  • FIG. 5 A first flow diagram for watermark detection is shown in Fig. 5. There are nSymbols watermark symbols in the watermark symbol alphabet. Watermark detection is carried out as foi ⁇ lows:
  • a result values array or block r of a cross cor ⁇ relation between a current section of the received signal and reference patterns is provided, for example by means of fast Fourier transform and inverse fast Fourier transform. Accordingly, there are nSymbols correlation result value arrays used for a watermark detection.
  • step 511 the correlation array values are sorted accord ⁇ ing to their magnitude, and a couple of n p maximal values are used as a peak vector.
  • the standard deviation ⁇ can be estimated either individually for each correlation result array, or by averaging over sets of correlation result arrays. Thereafter i is incremented and, as long as ⁇ ⁇ nSymbols in step 513 , the first i loop continues with step 51 1 .
  • pdf and Pf p values are evaluated.
  • PDF values g(v t ) are evaluated for nSymbols candidate watermark symbols based on equation ( 1 ) .
  • £ p (Wj) is obtained in step 53 by means of interpolation or extrapolation.
  • step 54 it is checked whether the values £ p (Wj) are small ⁇ er than a first threshold T min . If true, the corresponding candidate watermark is detected as the embedded one and is output in step 5 9 . If not true, i is incremented. In step 55 , if i is smaller equal nSymbols, the processing continues with step 52 .
  • step 57 the minimal Pf p val ⁇ ue for all candidate watermark symbols is then compared to a second threshold T max > T min . If the minimal Pf p value is smaller than T max , the symbol resulting in the minimal Pf p value is determined to be the embedded one and is output in step 5 9 . If the minimal Pf p value is not smaller than T max , it is decided in step 58 that no watermark is present in the received current signal section.
  • no watermark is present/detected means that none of the candidate watermarks is present or detected.
  • FIG. 7 A second flow diagram for watermark detection is shown in Fig. 7. There are nSymbols watermark symbols in the watermark symbol alphabet. Watermark detection is carried out as fol ⁇ lows :
  • step 711 the correlation array values are sorted accord ⁇ ing to their magnitude, and a couple of n p maximal values are used as a peak vector.
  • the peak vector peak values can be normalised in step 712, for all nSymbols peak vectors:
  • W; 1 ⁇ i ⁇ nSymbols , where v t denotes the peak vector ob ⁇ tained after sorting the correlation results array i and w t denotes the normalised peak vector i .
  • the standard deviation is estimated either individually for each set of correlation result values corresponding to individual candidate water- mark symbol, or by averaging over sets of correlation result values .
  • step 72 pdf and Pf p values are evaluated.
  • PDF values g( vi) are evaluated for nSymbols candidate watermark symbols based on equation (1) .
  • Pf P ( Vi) is obtained in step 73 by means of interpolation or extrapolation.
  • the symbol resulting in the minimal Pf p value is determined to be the embedded one and is output in step 7 9 . If the minimal Pf p value is not smaller than T max , it is decided in step 7 8 that no watermark is present in the received current signal section.
  • a received watermarked signal is re-sampled in an acquisition or re ⁇ ceiving section step or stage 61 , and thereafter may pass through a spectral shaping and/or whitening step or stage 62 .
  • correlation step or stage 63 it is correlated section by section with the nSymbols reference patterns.
  • a symbol detection or decision step or stage 64 de ⁇ termines, whether or not a corresponding watermark symbol is present in the current signal section.
  • a secret key was used to generate pseudo-random phases, from which related reference pattern bit sequences or symbols were generated and used for water ⁇ marking the audio signal.
  • these pseudo-random phases are generated in the same way in a cor- responding step or stage 65 , based on the same secret key.
  • related candidate reference patterns or symbols are generated in a reference pattern generation step or stage 66 and are used in step/stage 63 for checking whether or not a related watermark symbol is present in the current signal section of the received audio signal .
  • a look-up table 67 for probability distribution function val- ues and a look-up table 68 for false positive probabilities are used for the embedded watermark symbol determination as described above.
  • the described processing can be carried out by a single pro ⁇ cessor or electronic circuit, or by several processors or electronic circuits operating in parallel and/or operating on different parts of the complete processing.
  • the instructions for operating the processor or the proces ⁇ sors according to the described processing can be stored in one or more memories.
  • the at least one processor is config ⁇ ured to carry out these instructions.

Abstract

La présente invention concerne un procédé selon lequel on détermine à partir d'ensembles de valeurs de résultat l'incorporation ou non d'un symbole de tatouage numérique spécifique parmi un ou plusieurs symboles de tatouage numérique candidats dans un signal audio reçu. Pour tous les symboles de tatouage numérique candidats, à partir de chaque ensemble correspondant de valeurs de résultats de corrélation, un groupe (n p ) de valeurs maximales forment ensemble un vecteur de crête (v i . A partir des valeurs de crête normalisées (w i une fonction de distribution de probabilité (pdf, g(W i )) et une fonction de probabilité de faux positifs (P fp (w i )) sont calculées. Si les valeurs de la fonction de probabilité de faux positifs sont inférieures à une première valeur seuil (Tmin) / le symbole de tatouage numérique candidat courant est considéré comme un symbole de tatouage numérique vrai. Si tous les symboles de tatouage numérique candidats n'ont pas été traités, le symbole de tatouage numérique candidat suivant est sélectionné. Sinon, une valeur minimale (P* fp ) des fonctions de probabilité de faux positifs pour tous les symboles de tatouage numérique candidats est déterminée (76) et est comparée (77) avec une seconde valeur seuil (Tmax). Si elle est inférieure à la deuxième valeur seuil, le symbole de tatouage numérique candidat est sélectionné. Dans le cas contraire, il est déterminé qu'aucun symbole de tatouage numérique (78) n'est présent.
PCT/EP2015/070685 2014-09-23 2015-09-10 Procede et appareil pour la determination de l'incorporation ou non d'un symbole de tatouage numerique specifique parmi un ou plusieurs symboles de tatouage numerique candidats dans une section courante d'un signal audio recu WO2016045977A1 (fr)

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EP14306464.0A EP3001415A1 (fr) 2014-09-23 2014-09-23 Procédé et appareil permettant de déterminer si un symbole en filigrane spécifique à partir d'un ou de plusieurs symboles de filigranes candidats est incorporé dans une section présente d'un signal audio reçu
EP14306464.0 2014-09-23

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CN109299313A (zh) * 2018-08-03 2019-02-01 昆明理工大学 一种基于FP-growth的歌曲推荐方法

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EP2175443A1 (fr) * 2008-10-10 2010-04-14 Thomson Licensing Procédé et appareil pour la récupération de données de filigrane qui étaient intégrées dans un signal original en modifiant des sections dudit signal original en relation avec au moins deux séquences de données de références différentes
EP2387033A1 (fr) * 2010-05-11 2011-11-16 Thomson Licensing Procédé et appareil pour détecter lequel des symboles des données de filigrane est intégré dans un signal reçu

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EP2175443A1 (fr) * 2008-10-10 2010-04-14 Thomson Licensing Procédé et appareil pour la récupération de données de filigrane qui étaient intégrées dans un signal original en modifiant des sections dudit signal original en relation avec au moins deux séquences de données de références différentes
EP2387033A1 (fr) * 2010-05-11 2011-11-16 Thomson Licensing Procédé et appareil pour détecter lequel des symboles des données de filigrane est intégré dans un signal reçu

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MICHAEL ARNOLD ET AL: "Robust detection of audio watermarks after acoustic path transmission", PROCEEDINGS OF THE 12TH ACM WORKSHOP ON MULTIMEDIA AND SECURITY, MM&SEC '10, 1 January 2010 (2010-01-01), New York, New York, USA, pages 117, XP055071121, ISBN: 978-1-45-030286-9, DOI: 10.1145/1854229.1854253 *

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