A replay attack detection scheme based on perceptual image hashing
Abstract Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we prop...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hamadouche, Maamar [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, 83(2023), 3 vom: 12. Juni, Seite 8999-9031 |
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| Übergeordnetes Werk: |
volume:83 ; year:2023 ; number:3 ; day:12 ; month:06 ; pages:8999-9031 |
| Links: |
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| DOI / URN: |
10.1007/s11042-023-15300-5 |
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| Katalog-ID: |
SPR054305071 |
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| 520 | |a Abstract Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. | ||
| 650 | 4 | |a Digital replay attack |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Perceptual hashing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Content authentication |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Content identification |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Differential Luminance Block Means (DLBM) |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Normalization shifts |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Khalil, Zebbiche |4 aut | |
| 700 | 1 | |a TEBBI, Hanane |4 aut | |
| 700 | 1 | |a GUERROUMI, Mohamed |4 aut | |
| 700 | 1 | |a ZAFOUNE, Youcef |4 aut | |
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10.1007/s11042-023-15300-5 doi (DE-627)SPR054305071 (SPR)s11042-023-15300-5-e DE-627 ger DE-627 rakwb eng Hamadouche, Maamar verfasserin (orcid)0000-0003-3364-266X aut A replay attack detection scheme based on perceptual image hashing 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 Khalil, Zebbiche aut TEBBI, Hanane aut GUERROUMI, Mohamed aut ZAFOUNE, Youcef aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 3 vom: 12. Juni, Seite 8999-9031 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:3 day:12 month:06 pages:8999-9031 https://dx.doi.org/10.1007/s11042-023-15300-5 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 83 2023 3 12 06 8999-9031 |
| spelling |
10.1007/s11042-023-15300-5 doi (DE-627)SPR054305071 (SPR)s11042-023-15300-5-e DE-627 ger DE-627 rakwb eng Hamadouche, Maamar verfasserin (orcid)0000-0003-3364-266X aut A replay attack detection scheme based on perceptual image hashing 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 Khalil, Zebbiche aut TEBBI, Hanane aut GUERROUMI, Mohamed aut ZAFOUNE, Youcef aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 3 vom: 12. Juni, Seite 8999-9031 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:3 day:12 month:06 pages:8999-9031 https://dx.doi.org/10.1007/s11042-023-15300-5 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 83 2023 3 12 06 8999-9031 |
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10.1007/s11042-023-15300-5 doi (DE-627)SPR054305071 (SPR)s11042-023-15300-5-e DE-627 ger DE-627 rakwb eng Hamadouche, Maamar verfasserin (orcid)0000-0003-3364-266X aut A replay attack detection scheme based on perceptual image hashing 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 Khalil, Zebbiche aut TEBBI, Hanane aut GUERROUMI, Mohamed aut ZAFOUNE, Youcef aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 3 vom: 12. Juni, Seite 8999-9031 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:3 day:12 month:06 pages:8999-9031 https://dx.doi.org/10.1007/s11042-023-15300-5 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 83 2023 3 12 06 8999-9031 |
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10.1007/s11042-023-15300-5 doi (DE-627)SPR054305071 (SPR)s11042-023-15300-5-e DE-627 ger DE-627 rakwb eng Hamadouche, Maamar verfasserin (orcid)0000-0003-3364-266X aut A replay attack detection scheme based on perceptual image hashing 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 Khalil, Zebbiche aut TEBBI, Hanane aut GUERROUMI, Mohamed aut ZAFOUNE, Youcef aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 3 vom: 12. Juni, Seite 8999-9031 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:3 day:12 month:06 pages:8999-9031 https://dx.doi.org/10.1007/s11042-023-15300-5 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 83 2023 3 12 06 8999-9031 |
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10.1007/s11042-023-15300-5 doi (DE-627)SPR054305071 (SPR)s11042-023-15300-5-e DE-627 ger DE-627 rakwb eng Hamadouche, Maamar verfasserin (orcid)0000-0003-3364-266X aut A replay attack detection scheme based on perceptual image hashing 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 Khalil, Zebbiche aut TEBBI, Hanane aut GUERROUMI, Mohamed aut ZAFOUNE, Youcef aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 3 vom: 12. Juni, Seite 8999-9031 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:3 day:12 month:06 pages:8999-9031 https://dx.doi.org/10.1007/s11042-023-15300-5 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 83 2023 3 12 06 8999-9031 |
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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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. 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Hamadouche, Maamar |
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Hamadouche, Maamar misc Digital replay attack misc Perceptual hashing misc Content authentication misc Content identification misc Differential Luminance Block Means (DLBM) misc Normalization shifts A replay attack detection scheme based on perceptual image hashing |
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A replay attack detection scheme based on perceptual image hashing Digital replay attack (dpeaa)DE-He213 Perceptual hashing (dpeaa)DE-He213 Content authentication (dpeaa)DE-He213 Content identification (dpeaa)DE-He213 Differential Luminance Block Means (DLBM) (dpeaa)DE-He213 Normalization shifts (dpeaa)DE-He213 |
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misc Digital replay attack misc Perceptual hashing misc Content authentication misc Content identification misc Differential Luminance Block Means (DLBM) misc Normalization shifts |
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replay attack detection scheme based on perceptual image hashing |
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A replay attack detection scheme based on perceptual image hashing |
| abstract |
Abstract Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. © 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. © 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 Perceptual image hashing has mainly been used in the literature to authenticate images or to identify similar contents for image copy detection. In this work, we investigate the introduction of perceptual image hashing to protect biometric systems against digital replay attacks, and we propose a novel Replay Attack Detection (RAD) scheme that guarantees the authenticity of the received biometric image on the one hand, and identifies if it has been resubmitted on the other hand. The proposed scheme has the advantages of operating in an uncontrolled environment and does not need the cooperation of end users. It relies on the Challenge/Response principle and combines perceptual image hashing and data hiding techniques. Additionally, a new perceptual image hashing system that performs both content authentication and identification is proposed for fingerprint images. It is based on a shift-invariant calibration signal technique that uses the Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) of the Differential Block Luminance Mean (DBLM) features to compute the hash sequence. The performance of the proposed system has been evaluated through intensive experiments with real fingerprint images, and the obtained results have shown that the proposed system can provide high performance in terms of authentication and identification. Furthermore, the proposed system outperformed related state-of-the-art image hashing techniques. © 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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A replay attack detection scheme based on perceptual image hashing |
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Khalil, Zebbiche TEBBI, Hanane GUERROUMI, Mohamed ZAFOUNE, Youcef |
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Khalil, Zebbiche TEBBI, Hanane GUERROUMI, Mohamed ZAFOUNE, Youcef |
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10.1007/s11042-023-15300-5 |
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| score |
7.402135 |