Image steganalysis method based on cover selection and adaptive filtered residual network
Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some net...
Ausführliche Beschreibung
Autor*in: |
Ma, Yuanyuan [verfasserIn] Yang, Zenghao [verfasserIn] Li, Tao [verfasserIn] Xu, Lige [verfasserIn] Qiao, Yaqiong [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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Übergeordnetes Werk: |
Enthalten in: Computers & graphics - Amsterdam [u.a.] : Elsevier Science, 1975, 115, Seite 43-54 |
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Übergeordnetes Werk: |
volume:115 ; pages:43-54 |
DOI / URN: |
10.1016/j.cag.2023.06.034 |
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Katalog-ID: |
ELV065746236 |
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245 | 1 | 0 | |a Image steganalysis method based on cover selection and adaptive filtered residual network |
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520 | |a Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. | ||
650 | 4 | |a Steganalysis | |
650 | 4 | |a Cover selection | |
650 | 4 | |a Deep learning | |
650 | 4 | |a Adaptive filter | |
700 | 1 | |a Yang, Zenghao |e verfasserin |4 aut | |
700 | 1 | |a Li, Tao |e verfasserin |4 aut | |
700 | 1 | |a Xu, Lige |e verfasserin |4 aut | |
700 | 1 | |a Qiao, Yaqiong |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Computers & graphics |d Amsterdam [u.a.] : Elsevier Science, 1975 |g 115, Seite 43-54 |h Online-Ressource |w (DE-627)31622572X |w (DE-600)1499979-1 |w (DE-576)081984979 |7 nnns |
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allfields |
10.1016/j.cag.2023.06.034 doi (DE-627)ELV065746236 (ELSEVIER)S0097-8493(23)00129-2 DE-627 ger DE-627 rda eng 004 VZ 54.73 bkl Ma, Yuanyuan verfasserin aut Image steganalysis method based on cover selection and adaptive filtered residual network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. Steganalysis Cover selection Deep learning Adaptive filter Yang, Zenghao verfasserin aut Li, Tao verfasserin aut Xu, Lige verfasserin aut Qiao, Yaqiong verfasserin aut Enthalten in Computers & graphics Amsterdam [u.a.] : Elsevier Science, 1975 115, Seite 43-54 Online-Ressource (DE-627)31622572X (DE-600)1499979-1 (DE-576)081984979 nnns volume:115 pages:43-54 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 Computergraphik VZ AR 115 43-54 |
spelling |
10.1016/j.cag.2023.06.034 doi (DE-627)ELV065746236 (ELSEVIER)S0097-8493(23)00129-2 DE-627 ger DE-627 rda eng 004 VZ 54.73 bkl Ma, Yuanyuan verfasserin aut Image steganalysis method based on cover selection and adaptive filtered residual network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. Steganalysis Cover selection Deep learning Adaptive filter Yang, Zenghao verfasserin aut Li, Tao verfasserin aut Xu, Lige verfasserin aut Qiao, Yaqiong verfasserin aut Enthalten in Computers & graphics Amsterdam [u.a.] : Elsevier Science, 1975 115, Seite 43-54 Online-Ressource (DE-627)31622572X (DE-600)1499979-1 (DE-576)081984979 nnns volume:115 pages:43-54 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 Computergraphik VZ AR 115 43-54 |
allfields_unstemmed |
10.1016/j.cag.2023.06.034 doi (DE-627)ELV065746236 (ELSEVIER)S0097-8493(23)00129-2 DE-627 ger DE-627 rda eng 004 VZ 54.73 bkl Ma, Yuanyuan verfasserin aut Image steganalysis method based on cover selection and adaptive filtered residual network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. Steganalysis Cover selection Deep learning Adaptive filter Yang, Zenghao verfasserin aut Li, Tao verfasserin aut Xu, Lige verfasserin aut Qiao, Yaqiong verfasserin aut Enthalten in Computers & graphics Amsterdam [u.a.] : Elsevier Science, 1975 115, Seite 43-54 Online-Ressource (DE-627)31622572X (DE-600)1499979-1 (DE-576)081984979 nnns volume:115 pages:43-54 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 Computergraphik VZ AR 115 43-54 |
allfieldsGer |
10.1016/j.cag.2023.06.034 doi (DE-627)ELV065746236 (ELSEVIER)S0097-8493(23)00129-2 DE-627 ger DE-627 rda eng 004 VZ 54.73 bkl Ma, Yuanyuan verfasserin aut Image steganalysis method based on cover selection and adaptive filtered residual network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. Steganalysis Cover selection Deep learning Adaptive filter Yang, Zenghao verfasserin aut Li, Tao verfasserin aut Xu, Lige verfasserin aut Qiao, Yaqiong verfasserin aut Enthalten in Computers & graphics Amsterdam [u.a.] : Elsevier Science, 1975 115, Seite 43-54 Online-Ressource (DE-627)31622572X (DE-600)1499979-1 (DE-576)081984979 nnns volume:115 pages:43-54 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 Computergraphik VZ AR 115 43-54 |
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10.1016/j.cag.2023.06.034 doi (DE-627)ELV065746236 (ELSEVIER)S0097-8493(23)00129-2 DE-627 ger DE-627 rda eng 004 VZ 54.73 bkl Ma, Yuanyuan verfasserin aut Image steganalysis method based on cover selection and adaptive filtered residual network 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. Steganalysis Cover selection Deep learning Adaptive filter Yang, Zenghao verfasserin aut Li, Tao verfasserin aut Xu, Lige verfasserin aut Qiao, Yaqiong verfasserin aut Enthalten in Computers & graphics Amsterdam [u.a.] : Elsevier Science, 1975 115, Seite 43-54 Online-Ressource (DE-627)31622572X (DE-600)1499979-1 (DE-576)081984979 nnns volume:115 pages:43-54 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.73 Computergraphik VZ AR 115 43-54 |
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004 VZ 54.73 bkl Image steganalysis method based on cover selection and adaptive filtered residual network Steganalysis Cover selection Deep learning Adaptive filter |
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title |
Image steganalysis method based on cover selection and adaptive filtered residual network |
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Image steganalysis method based on cover selection and adaptive filtered residual network |
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Ma, Yuanyuan |
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Computers & graphics |
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Ma, Yuanyuan Yang, Zenghao Li, Tao Xu, Lige Qiao, Yaqiong |
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Ma, Yuanyuan |
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10.1016/j.cag.2023.06.034 |
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004 |
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title_sort |
image steganalysis method based on cover selection and adaptive filtered residual network |
title_auth |
Image steganalysis method based on cover selection and adaptive filtered residual network |
abstract |
Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. |
abstractGer |
Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. |
abstract_unstemmed |
Image steganalysis aims to detect secret messages embedded in digital images. Nowadays, deep learning-based image steganalysis methods have achieved promising detection performance. However, few existing deep learning-based steganalysis methods take cover selection into consideration. While some networks only utilize fixed filter kernels, which make little use of the learning ability of the network. To alleviate the issue, we propose an image steganalysis method based on cover selection and adaptive filtered residual networks (CSRNet), which can be used for stego images classification. Firstly, we devise a cover selection method, which is specifically divided into two parts. One part is a gray-level co-generation matrix-based (GLCM) multi-scale texture complexity measure to quantify texture complexity. And the other is an inverse power law function-based (IPLF) learning curve, which can contribute to determining the number of selected cover images. Secondly, we design a deep learning steganalysis network based on adaptive filtered kernel and residual learning, which employs the cover-selected images for training, and utilizes the network powerful classification ability. Finally, based on BOSSbase 1.01 and BOWS2, the experimental results show that compared with the classical and state-of-the-art steganalysis methods, the proposed method can not only significantly improve the detection accuracy, but also reduce time-space cost. |
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title_short |
Image steganalysis method based on cover selection and adaptive filtered residual network |
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Yang, Zenghao Li, Tao Xu, Lige Qiao, Yaqiong |
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