EP1971961A1 - Procede pour identifier un contenu marque - Google Patents

Procede pour identifier un contenu marque

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Publication number
EP1971961A1
EP1971961A1 EP06718417A EP06718417A EP1971961A1 EP 1971961 A1 EP1971961 A1 EP 1971961A1 EP 06718417 A EP06718417 A EP 06718417A EP 06718417 A EP06718417 A EP 06718417A EP 1971961 A1 EP1971961 A1 EP 1971961A1
Authority
EP
European Patent Office
Prior art keywords
prediction error
images
content
image
analysis
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP06718417A
Other languages
German (de)
English (en)
Other versions
EP1971961A4 (fr
Inventor
Dekun Zou
Yun-Qing Shi
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
New Jersey Institute of Technology
Original Assignee
New Jersey Institute of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by New Jersey Institute of Technology filed Critical New Jersey Institute of Technology
Publication of EP1971961A1 publication Critical patent/EP1971961A1/fr
Publication of EP1971961A4 publication Critical patent/EP1971961A4/fr
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Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0021—Image watermarking

Definitions

  • This application is related to classifying or identifying content, such as marked content, for example.
  • FIGS. 1A-C are schematic diagrams illustrating one embodiment of a predication error model as applied to content, such as an image.
  • Fridrich et al. have shown that the number of zeros in a block DCT domain of a stego-image will increase if the F5 embedding method is applied to generate the stego-image. This feature may be used to determine whether hidden messages have been embedded with the F5 method in content, for example.
  • Fridrich et al. have shown that the number of zeros in a block DCT domain of a stego-image will increase if the F5 embedding method is applied to generate the stego-image. This feature may be used to determine whether hidden messages have been embedded with the F5 method in content, for example.
  • There are other findings regarding steganalysis of particularly targeted data hiding methods See, for example, J. Fridrich, M. Goljan and R. Du, "Detecting LSB steganography in color and gray-scale images", Magazine of IEEE Multimedia Special Issue on Security, Oct.-Nov. 2001 , pp. 22-28; and R.Chandramouli and N.Memon, "
  • Lyu and Farid proposed a more general steganalysis method based at least in part on image high order statistics, derived from image decomposition with separable quadrature mirror filters.
  • image high order statistics derived from image decomposition with separable quadrature mirror filters.
  • the wavelet high- frequency subbands' high order statistics are extracted as features for steganalysis in this approach.
  • this approach has been shown differentiate stego-images from cover images with a certain success rate.
  • Data hiding methods addressed by this particular steganalysis primarily comprise least significant bit-plane (LSB) modification type steganographic tools.
  • LSB bit-plane
  • SS spread spectrum
  • a steganalysis system based at least in part on a 2-D Markov chain of thresholded prediction-error sets for content, such as images, for example, is described below, although claimed subject matter is not limited in scope in this respect.
  • content samples such as, for example, image pixels
  • a prediction-error image for example, is generated by subtracting the prediction value from the pixel value and thresholding.
  • Empirical transition matrixes along the horizontal, vertical and diagonal directions of Markov chains may, in such an embodiment serve as features for steganalysis.
  • a steganalysis system based at least in part on a Markov chain model of thresholded prediction-error images may be applied. Image pixels are predicted with the neighboring pixels. Prediction error in this particular embodiment is obtained by subtracting the prediction values from the pixel value. Though the range of the difference values is increased, the majority of the difference values may be concentrated in a relatively small range near zero owing to a correlation between neighboring pixels in unmarked images.
  • marked content refers to content in which data has been hidden so that it is not apparent that the content contains such hidden information.
  • unmarked or cover content refers to content in which data has not been hidden.
  • Large values in a prediction-error image may be attributed at least in part to image content rather than data hiding. Therefore, a threshold applied to prediction error may reduce or remove large values in the prediction error images, thus limiting the dynamic range of a prediction-error image.
  • prediction-error images may be modeled using a Markov chain.
  • An empirical transition matrix is calculated and serves as features for steganalysis. Owing at least in part to thresholding, the size of empirical transition matrixes is decreased to a manageable size for classifiers so that probabilities in the matrixes may be included in feature vectors.
  • an analysis of variance or other statistical approach may be applied. For example, an SVM process may be applied with both linear and non-linear kernels used for classification, as described in more detail below.
  • analysis of variance process refers to a process in which differences attributable to statistical variation are sufficiently distinguished from differences attributable to non-statistical variation that correlation, segmentation, analysis, classification or other characterization of the data based at least in part on such a process may be performed.
  • steganalysis may have a variety of meanings, for the purpose of this particular embodiment, it refers to a two-class pattern classification approach. For example, a test image may be classified as either a cover image, namely, information is not hidden in it, or a stego-image or marked image, which carries hidden data or hidden messages.
  • the classification comprises two parts, although claimed subject matter is not limited in scope to employing only two classifications.
  • the feature may represent the shape and color of an object.
  • other properties may provide useful information.
  • steganalysis for example, it is desirable to have a feature contain information about changes incurred by data hiding as opposed to information about the content of the image.
  • unmarked images may tend to exhibit particular properties, such as continuous, smooth, and having a correlation between neighboring pixels.
  • hidden data may be independent of the content itself.
  • a watermarking process for example, may change continuity with respect to the unmarked content because it may introduce some amount of random variation, for example. As a result, it may reduce correlation among adjacent pixels, bit-planes and image blocks.
  • this potential variation that may be attributed to data hiding is amplified. This may be accomplished by anyone of a number of possible approaches and claimed subject matter is not limited in scope to a particular approach. However, below, one particular embodiment for accomplishing this is described.
  • neighboring pixels may be used to predict the current pixel.
  • the predictions may be made in three directions. Again, for this embodiment, these directions include horizontal, vertical and diagonal, although in other embodiments other directions are possible.
  • prediction error may be estimated or obtained by subtracting a predicted pixel value from a original pixel value as shown in (1),
  • e h (i, j) indicates prediction error for pixel (i, j) along a horizontal direction
  • e v (i, j) indicates prediction error for pixel (i, j) along a vertical direction
  • ⁇ d (i, j) indicates prediction error for pixel (i, j) along a diagonal direction, respectively.
  • a threshold T may be adopted the prediction errors may be adjusted according to the following rule:
  • T may not comprise a fixed value. For example, it may vary with time, location, and a host of other potential factors.
  • large prediction errors may be treated as 0.
  • image pixels or other content samples may be regarded as smooth from the data hiding point of view.
  • the value range of a prediction-error image is [-T, T], with 2*T+1 possible values.
  • FIG. 1A is a schematic diagram illustrating an embodiment of transition model for horizontal prediction-error image E h , in which a Markov chain is modeled along the horizontal direction, for example.
  • FIG. 1 B and FIG. 1C are schematic diagrams illustrating corresponding embodiments for E v and E d, respectively.
  • elements of the empirical transition matrices for E h , E v and E d in this embodiment are employed as features.
  • one circle represents one pixel.
  • the diagrams show an image of size 8 by 8.
  • the arrows represent the state change in a Markov chain.
  • analysis of variance process refers to processes or techniques that may be applied so that differences attributable to statistical variation are sufficiently distinguished from differences attributable to non-statistical variation to correlate, segment, classify, analyze or otherwise characterize the data based at least in part on application of such processes or techniques.
  • examples without intending to limit the scope of claimed subject matter includes: artificial intelligence techniques and processes; neutral networks; genetic processes; heuristics; and support vector machines (SVM).
  • SVM may, for example, be employed to handle linear and non-linear cases or situations.
  • linear support vector processes may be formulated as follows. If a separating hyper-plane exists, training data satisfies the following constraints:
  • a Lagrangian formulation may likewise be constructed as follows:
  • CX 1 is the positive Lagrange multiplier introduced for inequality constraints, here (3) & (4).
  • the gradient of L with respect to w and b provides:
  • a sample z from testing data may be classified using w and b. For example, in one embodiment, if w'z + b is greater than or equal to zero, the image may be classified as having a hidden message. Otherwise, it may be classified as not containing a hidden message.
  • w'z + b is greater than or equal to zero, the image may be classified as having a hidden message. Otherwise, it may be classified as not containing a hidden message.
  • a "learning machine” may map input feature vectors to a higher dimensional space in which a linear hyper-plane may potentially be located.
  • a transformation from nonlinear feature space to linear higher dimensional space may be performed using a kernel function.
  • kernels include: linear, polynomial, radial basis function and sigmoid.
  • a linear kernel may be employed in connection with a linear SVM process, for example.
  • kernels may be employed in connection with a non-linear SVM process.
  • identifying or classifying marked content such as images, for example, it is desirable to construct and evaluate performance.
  • this is merely a particular embodiment for purposes of illustration and claimed subject matter is not limited in scope to this particular embodiment or approach.
  • Typical data hiding methods were applied to the images, such as: Cox et al.'s non-blind SS data hiding method, see I. J. Cox, J.Kilian, T.Leighton and T. Shamoon, "Secure spread spectrum watermarking for multimedia," IEEE Trans, on Image Processing, 6, 12, 1673-1687, (1997); Piva et al.'s blind SS, see A.Piva, M.Barni, E.Bartolini, V.Cappellini, "DCT-based watermark recovering without resorting to the uncorrupted original image", Proc. ICIP 97, vol.
  • the threshold 7 was set to be 4, although, as previously indicated, claimed subject matter is not limited in scope to a fixed threshold value, or an integer value as well.
  • Effective prediction error values in this example range from [-4 to 4], with 9 different values in total. Therefore, the dimension of the transition matrix is 9 by 9, which is 81 features for an error image. Since we have three error images in three different directions, the number of total features is 243 for an image in this particular example, although, again, claimed subject matter is not limited in scope in this respect.
  • TN stands for “True Negative”, here, the detection rate of original cover images.
  • TP stands for “True Positive”, here, the detection rate of stego-images.
  • Average is the arithmetic mean of these two rates. In other words, it is the overall correct classification rate for all test images.
  • this particular embodiment has a True Positive rate of over 90% for Cox's SS, Piva's blind SS, QIM and LSB with embedding strength over 0.1 bpp.
  • Embedded data here comprises images with sizes ranging from 32x32 to 194x194.
  • Corresponding embedding data rates are from 0.02 bpp to 0.9 bpp and detection rates range from 1.9% to 78%.
  • this particular embodiment appears to outperform the approach shown in Lyu and Farid.
  • one embodiment may be in hardware, such as implemented to operate on a device or combination of devices, for example, whereas another embodiment may be in software.
  • an embodiment may be implemented in firmware, or as any combination of hardware, software, and/or firmware, for example.
  • one embodiment may comprise one or more articles, such as a storage medium or storage media.
  • This storage media such as, one or more CD-ROMs and/or disks, for example, may have stored thereon instructions, that when executed by a system, such as a computer system, computing platform, or other system, for example, may result in an embodiment of a method in accordance with claimed subject matter being executed, such as one of the embodiments previously described, for example.
  • a computing platform may include one or more processing units or processors, one or more input/output devices, such as a display, a keyboard and/or a mouse, and/or one or more memories, such as static random access memory, dynamic random access memory, flash memory, and/or a hard drive.
  • a display may be employed to display one or more queries, such as those that may be interrelated, and or one or more tree expressions, although, again, claimed subject matter is not limited in scope to this example.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Editing Of Facsimile Originals (AREA)

Abstract

L’invention concerne, brièvement, selon un mode de réalisation, un procédé d'identification d'un contenu marqué.
EP06718417A 2006-01-13 2006-01-13 Procede pour identifier un contenu marque Withdrawn EP1971961A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2006/001338 WO2007081344A1 (fr) 2006-01-13 2006-01-13 Procédé pour identifier un contenu marqué

Publications (2)

Publication Number Publication Date
EP1971961A1 true EP1971961A1 (fr) 2008-09-24
EP1971961A4 EP1971961A4 (fr) 2011-06-29

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP06718417A Withdrawn EP1971961A4 (fr) 2006-01-13 2006-01-13 Procede pour identifier un contenu marque

Country Status (3)

Country Link
EP (1) EP1971961A4 (fr)
JP (1) JP2009524078A (fr)
WO (1) WO2007081344A1 (fr)

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5649068A (en) * 1993-07-27 1997-07-15 Lucent Technologies Inc. Pattern recognition system using support vectors
US5768438A (en) * 1994-10-19 1998-06-16 Matsushita Electric Industrial Co., Ltd. Image encoding/decoding device
US7054847B2 (en) * 2001-09-05 2006-05-30 Pavilion Technologies, Inc. System and method for on-line training of a support vector machine
JP2003344196A (ja) * 2002-05-24 2003-12-03 Denso Corp 乗員状態検知方法及び装置

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
LYU, S. AND FARID, H.: "Steganalysis Using Color Wavelet Statistics and One-Class Support Vector Machines", PROCEEDINGS OF THE SPIE, vol. 5306, no. 1, 19 January 2004 (2004-01-19), - 22 January 2004 (2004-01-22), pages 35-45, XP007918660, *
REGUNATHAN RADHAKRISHNAN ET AL: "Data Masking: A New Approach for Steganography?", THE JOURNAL OF VLSI SIGNAL PROCESSING, KLUWER ACADEMIC PUBLISHERS, BO, vol. 41, no. 3, 1 November 2005 (2005-11-01), pages 293-303, XP019216682, ISSN: 1573-109X, DOI: DOI:10.1007/S11265-005-4153-1 *
See also references of WO2007081344A1 *

Also Published As

Publication number Publication date
EP1971961A4 (fr) 2011-06-29
WO2007081344A1 (fr) 2007-07-19
JP2009524078A (ja) 2009-06-25

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