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

Gespeichert in:

Autor*in:	Ma, Yuanyuan [verfasserIn] Yang, Zenghao [verfasserIn] Li, Tao [verfasserIn] Xu, Lige [verfasserIn] Qiao, Yaqiong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Steganalysis Cover selection Deep learning Adaptive filter

Übergeordnetes Werk:	Enthalten in: Computers & graphics - Amsterdam [u.a.] : Elsevier Science, 1975, 115, Seite 43-54
Übergeordnetes Werk:	volume:115 ; pages:43-54

DOI / URN:	10.1016/j.cag.2023.06.034

Katalog-ID:	ELV065746236

Internformat


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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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912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
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912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2009
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912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
912			\|a GBV_ILN_2110
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2232
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
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912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
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Indexfelder

author_variant	y m ym z y zy t l tl l x lx y q yq
matchkey_str	mayuanyuanyangzenghaolitaoxuligeqiaoyaqi:2023----:mgseaayimtobsdnoeslcinnaatvf
hierarchy_sort_str	2023
bklnumber	54.73
publishDate	2023
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
allfieldsSound	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
language	English
source	Enthalten in Computers & graphics 115, Seite 43-54 volume:115 pages:43-54
sourceStr	Enthalten in Computers & graphics 115, Seite 43-54 volume:115 pages:43-54
format_phy_str_mv	Article
bklname	Computergraphik
institution	findex.gbv.de
topic_facet	Steganalysis Cover selection Deep learning Adaptive filter
dewey-raw	004
isfreeaccess_bool	false
container_title	Computers & graphics
authorswithroles_txt_mv	Ma, Yuanyuan @@aut@@ Yang, Zenghao @@aut@@ Li, Tao @@aut@@ Xu, Lige @@aut@@ Qiao, Yaqiong @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	31622572X
dewey-sort	14
id	ELV065746236
language_de	englisch
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author	Ma, Yuanyuan
spellingShingle	Ma, Yuanyuan ddc 004 bkl 54.73 misc Steganalysis misc Cover selection misc Deep learning misc Adaptive filter Image steganalysis method based on cover selection and adaptive filtered residual network
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topic_title	004 VZ 54.73 bkl Image steganalysis method based on cover selection and adaptive filtered residual network Steganalysis Cover selection Deep learning Adaptive filter
topic	ddc 004 bkl 54.73 misc Steganalysis misc Cover selection misc Deep learning misc Adaptive filter
topic_unstemmed	ddc 004 bkl 54.73 misc Steganalysis misc Cover selection misc Deep learning misc Adaptive filter
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title	Image steganalysis method based on cover selection and adaptive filtered residual network
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title_full	Image steganalysis method based on cover selection and adaptive filtered residual network
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author-letter	Ma, Yuanyuan
doi_str_mv	10.1016/j.cag.2023.06.034
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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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author2	Yang, Zenghao Li, Tao Xu, Lige Qiao, Yaqiong
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doi_str	10.1016/j.cag.2023.06.034
up_date	2024-07-07T00:06:24.098Z
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Image steganalysis method based on cover selection and adaptive filtered residual network

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