Variable multi-dimensional co-occurrence for steganalysis

Licong Chen, Yun-Qing Shi, Patchara Sutthiwan

Research output: Contribution to journalConference article

4 Citations (Scopus)

Abstract

In this paper, a novel multidimensional co-occurrence histogram scheme has been presented, in which the numbers of quantization levels for the elements in the co-occurrence are variable according to the distance among the elements, referred to as the co-occurrence with variable number of quantization levels, abbreviated as the VNQL cooccurrence or variable co-occurrence. Specifically, the longer the distance the smaller the number of quantization levels used. The dimensionality of the variable co-occurrence is therefore flexible and much smaller than that used in the traditional multidimensional co-occurrence of the same orders. Furthermore, the symmetry existed in the multidimensional cooccurrence is skillfully utilized in the proposed variable co-occurrence. Consequently, the feature dimensionality has been lowered dramatically. Thus, the fourth order variable co-occurrence applied to the residuals used in the spatial rich model results in 1, 704 features, which work with a G-SVM classifier, can achieve an average detection rate similar to that of achieved by the TOP10 (dimension approximately 3, 300) in the spatial rich model with a G-SVM classifier against the HUGO at 0.4 bpp on the same setup. The time consumed by the proposed 1, 704 features is much shorter that used by the latter. Furthermore, the performance achieved by the proposed 1, 704 together with a G-SVM classifier is on par with that achieved by the TOP39 with 12, 753 features and an ensemble classifier on the same setup against two steganographic schemes, the HUGO and the edge adaptive. With another proposed set of 1, 977 features, generated from the residuals used in the spatial rich model and from a few newly formulated residuals, and the G-SVM classifier, the performance achieved is better than that achieved by using the TOP39 feature-set with ensemble classifier on the same setup against the HUGO.

Original languageEnglish (US)
Article numberA43
Pages (from-to)559-573
Number of pages15
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9023
DOIs
StatePublished - Jan 1 2015
Event13th International Workshop on Digital-Forensics and Watermarking , IWDW 2014 - Taipei, Taiwan, Province of China
Duration: Oct 1 2014Oct 4 2014

Fingerprint

Steganalysis
Classifiers
Spatial Model
Classifier
Ensemble Classifier
Quantization
Dimensionality
Histogram
Fourth Order
Symmetry

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Co-occurrence
  • High order co-occurrence
  • Steganalysis
  • Steganography
  • Variable cooccurrence

Cite this

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title = "Variable multi-dimensional co-occurrence for steganalysis",
abstract = "In this paper, a novel multidimensional co-occurrence histogram scheme has been presented, in which the numbers of quantization levels for the elements in the co-occurrence are variable according to the distance among the elements, referred to as the co-occurrence with variable number of quantization levels, abbreviated as the VNQL cooccurrence or variable co-occurrence. Specifically, the longer the distance the smaller the number of quantization levels used. The dimensionality of the variable co-occurrence is therefore flexible and much smaller than that used in the traditional multidimensional co-occurrence of the same orders. Furthermore, the symmetry existed in the multidimensional cooccurrence is skillfully utilized in the proposed variable co-occurrence. Consequently, the feature dimensionality has been lowered dramatically. Thus, the fourth order variable co-occurrence applied to the residuals used in the spatial rich model results in 1, 704 features, which work with a G-SVM classifier, can achieve an average detection rate similar to that of achieved by the TOP10 (dimension approximately 3, 300) in the spatial rich model with a G-SVM classifier against the HUGO at 0.4 bpp on the same setup. The time consumed by the proposed 1, 704 features is much shorter that used by the latter. Furthermore, the performance achieved by the proposed 1, 704 together with a G-SVM classifier is on par with that achieved by the TOP39 with 12, 753 features and an ensemble classifier on the same setup against two steganographic schemes, the HUGO and the edge adaptive. With another proposed set of 1, 977 features, generated from the residuals used in the spatial rich model and from a few newly formulated residuals, and the G-SVM classifier, the performance achieved is better than that achieved by using the TOP39 feature-set with ensemble classifier on the same setup against the HUGO.",
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Variable multi-dimensional co-occurrence for steganalysis. / Chen, Licong; Shi, Yun-Qing; Sutthiwan, Patchara.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9023, A43, 01.01.2015, p. 559-573.

Research output: Contribution to journalConference article

TY - JOUR

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AU - Chen, Licong

AU - Shi, Yun-Qing

AU - Sutthiwan, Patchara

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N2 - In this paper, a novel multidimensional co-occurrence histogram scheme has been presented, in which the numbers of quantization levels for the elements in the co-occurrence are variable according to the distance among the elements, referred to as the co-occurrence with variable number of quantization levels, abbreviated as the VNQL cooccurrence or variable co-occurrence. Specifically, the longer the distance the smaller the number of quantization levels used. The dimensionality of the variable co-occurrence is therefore flexible and much smaller than that used in the traditional multidimensional co-occurrence of the same orders. Furthermore, the symmetry existed in the multidimensional cooccurrence is skillfully utilized in the proposed variable co-occurrence. Consequently, the feature dimensionality has been lowered dramatically. Thus, the fourth order variable co-occurrence applied to the residuals used in the spatial rich model results in 1, 704 features, which work with a G-SVM classifier, can achieve an average detection rate similar to that of achieved by the TOP10 (dimension approximately 3, 300) in the spatial rich model with a G-SVM classifier against the HUGO at 0.4 bpp on the same setup. The time consumed by the proposed 1, 704 features is much shorter that used by the latter. Furthermore, the performance achieved by the proposed 1, 704 together with a G-SVM classifier is on par with that achieved by the TOP39 with 12, 753 features and an ensemble classifier on the same setup against two steganographic schemes, the HUGO and the edge adaptive. With another proposed set of 1, 977 features, generated from the residuals used in the spatial rich model and from a few newly formulated residuals, and the G-SVM classifier, the performance achieved is better than that achieved by using the TOP39 feature-set with ensemble classifier on the same setup against the HUGO.

AB - In this paper, a novel multidimensional co-occurrence histogram scheme has been presented, in which the numbers of quantization levels for the elements in the co-occurrence are variable according to the distance among the elements, referred to as the co-occurrence with variable number of quantization levels, abbreviated as the VNQL cooccurrence or variable co-occurrence. Specifically, the longer the distance the smaller the number of quantization levels used. The dimensionality of the variable co-occurrence is therefore flexible and much smaller than that used in the traditional multidimensional co-occurrence of the same orders. Furthermore, the symmetry existed in the multidimensional cooccurrence is skillfully utilized in the proposed variable co-occurrence. Consequently, the feature dimensionality has been lowered dramatically. Thus, the fourth order variable co-occurrence applied to the residuals used in the spatial rich model results in 1, 704 features, which work with a G-SVM classifier, can achieve an average detection rate similar to that of achieved by the TOP10 (dimension approximately 3, 300) in the spatial rich model with a G-SVM classifier against the HUGO at 0.4 bpp on the same setup. The time consumed by the proposed 1, 704 features is much shorter that used by the latter. Furthermore, the performance achieved by the proposed 1, 704 together with a G-SVM classifier is on par with that achieved by the TOP39 with 12, 753 features and an ensemble classifier on the same setup against two steganographic schemes, the HUGO and the edge adaptive. With another proposed set of 1, 977 features, generated from the residuals used in the spatial rich model and from a few newly formulated residuals, and the G-SVM classifier, the performance achieved is better than that achieved by using the TOP39 feature-set with ensemble classifier on the same setup against the HUGO.

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