Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations

Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Roy, Moumita [verfasserIn] Thounaojam, Dalton Meitei Pal, Shyamosree

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Perceptual image hashing KAZE features Key point descriptor Manipulations

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 81(2022), 20 vom: 01. Apr., Seite 29045-29073
Übergeordnetes Werk:	volume:81 ; year:2022 ; number:20 ; day:01 ; month:04 ; pages:29045-29073

Links:	Volltext

DOI / URN:	10.1007/s11042-022-12626-4

Katalog-ID:	SPR047666250

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520			\|a Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness.
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allfields	10.1007/s11042-022-12626-4 doi (DE-627)SPR047666250 (SPR)s11042-022-12626-4-e DE-627 ger DE-627 rakwb eng Roy, Moumita verfasserin (orcid)0000-0001-6835-5471 aut Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations 2022 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 2022 Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213 Thounaojam, Dalton Meitei aut Pal, Shyamosree aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 01. Apr., Seite 29045-29073 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073 https://dx.doi.org/10.1007/s11042-022-12626-4 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 81 2022 20 01 04 29045-29073
spelling	10.1007/s11042-022-12626-4 doi (DE-627)SPR047666250 (SPR)s11042-022-12626-4-e DE-627 ger DE-627 rakwb eng Roy, Moumita verfasserin (orcid)0000-0001-6835-5471 aut Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations 2022 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 2022 Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213 Thounaojam, Dalton Meitei aut Pal, Shyamosree aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 01. Apr., Seite 29045-29073 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073 https://dx.doi.org/10.1007/s11042-022-12626-4 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 81 2022 20 01 04 29045-29073
allfields_unstemmed	10.1007/s11042-022-12626-4 doi (DE-627)SPR047666250 (SPR)s11042-022-12626-4-e DE-627 ger DE-627 rakwb eng Roy, Moumita verfasserin (orcid)0000-0001-6835-5471 aut Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations 2022 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 2022 Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213 Thounaojam, Dalton Meitei aut Pal, Shyamosree aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 01. Apr., Seite 29045-29073 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073 https://dx.doi.org/10.1007/s11042-022-12626-4 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 81 2022 20 01 04 29045-29073
allfieldsGer	10.1007/s11042-022-12626-4 doi (DE-627)SPR047666250 (SPR)s11042-022-12626-4-e DE-627 ger DE-627 rakwb eng Roy, Moumita verfasserin (orcid)0000-0001-6835-5471 aut Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations 2022 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 2022 Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213 Thounaojam, Dalton Meitei aut Pal, Shyamosree aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 01. Apr., Seite 29045-29073 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073 https://dx.doi.org/10.1007/s11042-022-12626-4 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 81 2022 20 01 04 29045-29073
allfieldsSound	10.1007/s11042-022-12626-4 doi (DE-627)SPR047666250 (SPR)s11042-022-12626-4-e DE-627 ger DE-627 rakwb eng Roy, Moumita verfasserin (orcid)0000-0001-6835-5471 aut Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations 2022 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 2022 Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213 Thounaojam, Dalton Meitei aut Pal, Shyamosree aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 81(2022), 20 vom: 01. Apr., Seite 29045-29073 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073 https://dx.doi.org/10.1007/s11042-022-12626-4 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 81 2022 20 01 04 29045-29073
language	English
source	Enthalten in Multimedia tools and applications 81(2022), 20 vom: 01. Apr., Seite 29045-29073 volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073
sourceStr	Enthalten in Multimedia tools and applications 81(2022), 20 vom: 01. Apr., Seite 29045-29073 volume:81 year:2022 number:20 day:01 month:04 pages:29045-29073
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Perceptual image hashing KAZE features Key point descriptor Manipulations
isfreeaccess_bool	false
container_title	Multimedia tools and applications
authorswithroles_txt_mv	Roy, Moumita @@aut@@ Thounaojam, Dalton Meitei @@aut@@ Pal, Shyamosree @@aut@@
publishDateDaySort_date	2022-04-01T00:00:00Z
hierarchy_top_id	27135030X
id	SPR047666250
language_de	englisch
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author	Roy, Moumita
spellingShingle	Roy, Moumita misc Perceptual image hashing misc KAZE features misc Key point descriptor misc Manipulations Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations
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topic_title	Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations Perceptual image hashing (dpeaa)DE-He213 KAZE features (dpeaa)DE-He213 Key point descriptor (dpeaa)DE-He213 Manipulations (dpeaa)DE-He213
topic	misc Perceptual image hashing misc KAZE features misc Key point descriptor misc Manipulations
topic_unstemmed	misc Perceptual image hashing misc KAZE features misc Key point descriptor misc Manipulations
topic_browse	misc Perceptual image hashing misc KAZE features misc Key point descriptor misc Manipulations
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title	Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations
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title_full	Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations
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author_browse	Roy, Moumita Thounaojam, Dalton Meitei Pal, Shyamosree
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author-letter	Roy, Moumita
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title_sort	perceptual hashing scheme using kaze feature descriptors for combinatorial manipulations
title_auth	Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations
abstract	Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstractGer	Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
abstract_unstemmed	Abstract Perceptual image hashing methods utilize the visual phenomenon of the images and produce a fixed-length hash function and this hash value can be utilized for digital signature of an image. It can be used to show robustness against the digital manipulations done on the image and hence can be of use in different applications, viz., image indexing, tamper detection, etc. But to generate an efficient hash function is scarce as there is an inverse relationship of perceptual robustness and discrimination capability criteria. In this paper, we propose a robust and discrimination capable hash function by considering KAZE point feature descriptor for combinatorial manipulations. The KAZE detectors are used to find the stable key points of the image and then the three strongest regions are considered based on the strongest three key points of the image. Using these points, the features are generated and finally, the local features are used to generate the hash function and this hash function not only provides a good discrimination capable value with good robustness but also shows good results for double attacks and multiple combinations of attacks. Moreover, it outperforms the state-of-the-art algorithms in consideration for performances between discrimination capability and perceptual robustness. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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container_issue	20
title_short	Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations
url	https://dx.doi.org/10.1007/s11042-022-12626-4
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author2	Thounaojam, Dalton Meitei Pal, Shyamosree
author2Str	Thounaojam, Dalton Meitei Pal, Shyamosree
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doi_str	10.1007/s11042-022-12626-4
up_date	2024-07-03T14:12:35.463Z
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Perceptual hashing scheme using KAZE feature descriptors for combinatorial manipulations

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