Image steganography based on difference of Gaussians edge detection
Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Patwari, Biswajit [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 82(2023), 28 vom: 24. Apr., Seite 43759-43779 |
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| Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:28 ; day:24 ; month:04 ; pages:43759-43779 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-15360-7 |
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| Katalog-ID: |
SPR053562828 |
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| 520 | |a Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. | ||
| 650 | 4 | |a Steganography |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Edge detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Difference of Gaussians |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Embedding |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Extraction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Payload |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nandi, Utpal |4 aut | |
| 700 | 1 | |a Ghosal, Sudipta Kr |0 (orcid)0000-0002-8697-0803 |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 |g 82(2023), 28 vom: 24. Apr., Seite 43759-43779 |w (DE-627)27135030X |w (DE-600)1479928-5 |x 1573-7721 |7 nnns |
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10.1007/s11042-023-15360-7 doi (DE-627)SPR053562828 (SPR)s11042-023-15360-7-e DE-627 ger DE-627 rakwb eng Patwari, Biswajit verfasserin aut Image steganography based on difference of Gaussians edge detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 Nandi, Utpal aut Ghosal, Sudipta Kr (orcid)0000-0002-8697-0803 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 24. Apr., Seite 43759-43779 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:24 month:04 pages:43759-43779 https://dx.doi.org/10.1007/s11042-023-15360-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 24 04 43759-43779 |
| spelling |
10.1007/s11042-023-15360-7 doi (DE-627)SPR053562828 (SPR)s11042-023-15360-7-e DE-627 ger DE-627 rakwb eng Patwari, Biswajit verfasserin aut Image steganography based on difference of Gaussians edge detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 Nandi, Utpal aut Ghosal, Sudipta Kr (orcid)0000-0002-8697-0803 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 24. Apr., Seite 43759-43779 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:24 month:04 pages:43759-43779 https://dx.doi.org/10.1007/s11042-023-15360-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 24 04 43759-43779 |
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10.1007/s11042-023-15360-7 doi (DE-627)SPR053562828 (SPR)s11042-023-15360-7-e DE-627 ger DE-627 rakwb eng Patwari, Biswajit verfasserin aut Image steganography based on difference of Gaussians edge detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 Nandi, Utpal aut Ghosal, Sudipta Kr (orcid)0000-0002-8697-0803 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 24. Apr., Seite 43759-43779 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:24 month:04 pages:43759-43779 https://dx.doi.org/10.1007/s11042-023-15360-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 24 04 43759-43779 |
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10.1007/s11042-023-15360-7 doi (DE-627)SPR053562828 (SPR)s11042-023-15360-7-e DE-627 ger DE-627 rakwb eng Patwari, Biswajit verfasserin aut Image steganography based on difference of Gaussians edge detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 Nandi, Utpal aut Ghosal, Sudipta Kr (orcid)0000-0002-8697-0803 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 24. Apr., Seite 43759-43779 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:24 month:04 pages:43759-43779 https://dx.doi.org/10.1007/s11042-023-15360-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 24 04 43759-43779 |
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10.1007/s11042-023-15360-7 doi (DE-627)SPR053562828 (SPR)s11042-023-15360-7-e DE-627 ger DE-627 rakwb eng Patwari, Biswajit verfasserin aut Image steganography based on difference of Gaussians edge detection 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 Nandi, Utpal aut Ghosal, Sudipta Kr (orcid)0000-0002-8697-0803 aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 28 vom: 24. Apr., Seite 43759-43779 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:28 day:24 month:04 pages:43759-43779 https://dx.doi.org/10.1007/s11042-023-15360-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 82 2023 28 24 04 43759-43779 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Steganography</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Edge detection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Difference of Gaussians</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Embedding</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Extraction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Payload</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nandi, Utpal</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghosal, Sudipta Kr</subfield><subfield code="0">(orcid)0000-0002-8697-0803</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995</subfield><subfield code="g">82(2023), 28 vom: 24. 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Patwari, Biswajit |
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Patwari, Biswajit misc Steganography misc Edge detection misc Difference of Gaussians misc Embedding misc Extraction misc Payload Image steganography based on difference of Gaussians edge detection |
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Image steganography based on difference of Gaussians edge detection Steganography (dpeaa)DE-He213 Edge detection (dpeaa)DE-He213 Difference of Gaussians (dpeaa)DE-He213 Embedding (dpeaa)DE-He213 Extraction (dpeaa)DE-He213 Payload (dpeaa)DE-He213 |
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Image steganography based on difference of Gaussians edge detection |
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Image steganography based on difference of Gaussians edge detection |
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Patwari, Biswajit |
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Patwari, Biswajit Nandi, Utpal Ghosal, Sudipta Kr |
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image steganography based on difference of gaussians edge detection |
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Image steganography based on difference of Gaussians edge detection |
| abstract |
Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This paper introduces an edge-based image Steganography scheme in which the pixels of the cover images are categorized into two classes: edge and non-edge. In general, the edge pixels hide more secret bits compared to non-edge pixels due to the following two reasons: noisy nature and high tolerance level. The edge pixels are perceived as noisy due to the variation in intensities with respect to the neighboring pixels and hence it is difficult to model. Further the tolerance level of edge pixels is usually high compared to non-edge pixels on equivalent alteration. These two reasons motivate us to propose a new image Steganography method based on Difference of Gaussians (DoG) Edge detection. The proposed scheme has three major phases: pre-processing cum edge detection, embedding and extraction. In the leading two phases, we obtain the edge image from the cover image and then embed secret bits into the non-edge and edge pixels with X:Y ratio, where for all X, Y, %$1\le \text{X}\le 3 \ \text{and} \ 2\le \text{Y}\le (\text{X}+1)%$. In the ultimate phase, we extract the secret bits from the Stego-image. The extracted secret bits helps us to regenerate the secret image which was embedded earlier. The experimental result confirms that the proposed technique offers variable payload and acceptable visual clarity. The comparison results also ensure that the proposed method is superior to the conventional edge detection based Steganography schemes as far as the payload is concerned. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| container_issue |
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| title_short |
Image steganography based on difference of Gaussians edge detection |
| url |
https://dx.doi.org/10.1007/s11042-023-15360-7 |
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| author2 |
Nandi, Utpal Ghosal, Sudipta Kr |
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Nandi, Utpal Ghosal, Sudipta Kr |
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| doi_str |
10.1007/s11042-023-15360-7 |
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2024-07-03T20:25:30.479Z |
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| score |
7.402276 |