A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix
Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – c...
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Gespeichert in:
| Autor*in: |
Lu, Jicang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security - Xue, Liming ELSEVIER, 2023, the international journal of digital forensics & incident response, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:12 ; year:2015 ; pages:1-14 ; extent:14 |
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| DOI / URN: |
10.1016/j.diin.2014.12.001 |
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ELV028886143 |
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| 520 | |a Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. | ||
| 520 | |a Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. | ||
| 650 | 7 | |a Markov transition probability matrix |2 Elsevier | |
| 650 | 7 | |a Steganalysis |2 Elsevier | |
| 650 | 7 | |a Feature comparison |2 Elsevier | |
| 650 | 7 | |a Co-occurrence matrix |2 Elsevier | |
| 650 | 7 | |a JPEG |2 Elsevier | |
| 700 | 1 | |a Liu, Fenlin |4 oth | |
| 700 | 1 | |a Luo, Xiangyang |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Xue, Liming ELSEVIER |t Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security |d 2023 |d the international journal of digital forensics & incident response |g Amsterdam [u.a.] |w (DE-627)ELV009711430 |
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10.1016/j.diin.2014.12.001 doi GBVA2015009000006.pica (DE-627)ELV028886143 (ELSEVIER)S1742-2876(14)00119-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 620 VZ 83.65 bkl Lu, Jicang verfasserin aut A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix 2015transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Markov transition probability matrix Elsevier Steganalysis Elsevier Feature comparison Elsevier Co-occurrence matrix Elsevier JPEG Elsevier Liu, Fenlin oth Luo, Xiangyang oth Enthalten in Elsevier Xue, Liming ELSEVIER Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security 2023 the international journal of digital forensics & incident response Amsterdam [u.a.] (DE-627)ELV009711430 volume:12 year:2015 pages:1-14 extent:14 https://doi.org/10.1016/j.diin.2014.12.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 12 2015 1-14 14 045F 610 |
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10.1016/j.diin.2014.12.001 doi GBVA2015009000006.pica (DE-627)ELV028886143 (ELSEVIER)S1742-2876(14)00119-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 620 VZ 83.65 bkl Lu, Jicang verfasserin aut A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix 2015transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Markov transition probability matrix Elsevier Steganalysis Elsevier Feature comparison Elsevier Co-occurrence matrix Elsevier JPEG Elsevier Liu, Fenlin oth Luo, Xiangyang oth Enthalten in Elsevier Xue, Liming ELSEVIER Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security 2023 the international journal of digital forensics & incident response Amsterdam [u.a.] (DE-627)ELV009711430 volume:12 year:2015 pages:1-14 extent:14 https://doi.org/10.1016/j.diin.2014.12.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 12 2015 1-14 14 045F 610 |
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10.1016/j.diin.2014.12.001 doi GBVA2015009000006.pica (DE-627)ELV028886143 (ELSEVIER)S1742-2876(14)00119-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 620 VZ 83.65 bkl Lu, Jicang verfasserin aut A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix 2015transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Markov transition probability matrix Elsevier Steganalysis Elsevier Feature comparison Elsevier Co-occurrence matrix Elsevier JPEG Elsevier Liu, Fenlin oth Luo, Xiangyang oth Enthalten in Elsevier Xue, Liming ELSEVIER Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security 2023 the international journal of digital forensics & incident response Amsterdam [u.a.] (DE-627)ELV009711430 volume:12 year:2015 pages:1-14 extent:14 https://doi.org/10.1016/j.diin.2014.12.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 12 2015 1-14 14 045F 610 |
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10.1016/j.diin.2014.12.001 doi GBVA2015009000006.pica (DE-627)ELV028886143 (ELSEVIER)S1742-2876(14)00119-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 620 VZ 83.65 bkl Lu, Jicang verfasserin aut A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix 2015transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Markov transition probability matrix Elsevier Steganalysis Elsevier Feature comparison Elsevier Co-occurrence matrix Elsevier JPEG Elsevier Liu, Fenlin oth Luo, Xiangyang oth Enthalten in Elsevier Xue, Liming ELSEVIER Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security 2023 the international journal of digital forensics & incident response Amsterdam [u.a.] (DE-627)ELV009711430 volume:12 year:2015 pages:1-14 extent:14 https://doi.org/10.1016/j.diin.2014.12.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 12 2015 1-14 14 045F 610 |
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10.1016/j.diin.2014.12.001 doi GBVA2015009000006.pica (DE-627)ELV028886143 (ELSEVIER)S1742-2876(14)00119-4 DE-627 ger DE-627 rakwb eng 610 610 DE-600 620 VZ 83.65 bkl Lu, Jicang verfasserin aut A study on JPEG steganalytic features: Co-occurrence matrix vs. Markov transition probability matrix 2015transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. Markov transition probability matrix Elsevier Steganalysis Elsevier Feature comparison Elsevier Co-occurrence matrix Elsevier JPEG Elsevier Liu, Fenlin oth Luo, Xiangyang oth Enthalten in Elsevier Xue, Liming ELSEVIER Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security 2023 the international journal of digital forensics & incident response Amsterdam [u.a.] (DE-627)ELV009711430 volume:12 year:2015 pages:1-14 extent:14 https://doi.org/10.1016/j.diin.2014.12.001 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 12 2015 1-14 14 045F 610 |
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Enthalten in Applying GeoDetector to disentangle the contributions of the 4-As evaluation indicators to the spatial differentiation of coal resource security Amsterdam [u.a.] volume:12 year:2015 pages:1-14 extent:14 |
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Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. |
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Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. |
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Statistical feature selection is a key issue affecting the performance of steganalytic methods. In this paper, a performance comparison method for different types of image steganalytic features was proposed firstly based on the changing rates. Then, for two types of typical steganalytic features – co-occurrence matrix and Markov transition probability matrix, the performances of them were discussed and theoretically compared for detecting two types of well-known JPEG steganography that preserve DCT coefficients histogram and lead the histogram to shrink respectively. At last, a conclusion on the sensitivity comparison between components of these two types of features was derived: for the steganography that preserve the histogram, their sensitivities are comparable to each other; whereas for the other one (such as the steganography that subtract 1 from absolute value of the coefficient), different feature components have different sensitivities, on the basis of that, a new steganalytic feature could be obtained by fusing better components. Experimental results based on detection of three typical JPEG steganography (F5, Outguess and MB1) verified the theoretical comparison results, and showed that the detection accuracy of the fused new feature outperforms that of existing typical features. |
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