Thresholding binary coding for image forensics of weak sharpening
Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Ping [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis - Zeng, C. ELSEVIER, 2014, theory, techniques & applications, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:88 ; year:2020 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.image.2020.115956 |
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| 520 | |a Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. | ||
| 520 | |a Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. | ||
| 650 | 7 | |a Image forensics |2 Elsevier | |
| 650 | 7 | |a Unsharp mask |2 Elsevier | |
| 650 | 7 | |a Thresholding binary coding |2 Elsevier | |
| 650 | 7 | |a Weak sharpening |2 Elsevier | |
| 650 | 7 | |a Texture pattern mapping |2 Elsevier | |
| 700 | 1 | |a Liu, Fenlin |4 oth | |
| 700 | 1 | |a Yang, Chunfang |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Zeng, C. ELSEVIER |t Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis |d 2014 |d theory, techniques & applications |g Amsterdam [u.a.] |w (DE-627)ELV017872103 |
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10.1016/j.image.2020.115956 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001132.pica (DE-627)ELV05136235X (ELSEVIER)S0923-5965(20)30136-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Wang, Ping verfasserin aut Thresholding binary coding for image forensics of weak sharpening 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics Elsevier Unsharp mask Elsevier Thresholding binary coding Elsevier Weak sharpening Elsevier Texture pattern mapping Elsevier Liu, Fenlin oth Yang, Chunfang oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:88 year:2020 pages:0 https://doi.org/10.1016/j.image.2020.115956 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 88 2020 0 |
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10.1016/j.image.2020.115956 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001132.pica (DE-627)ELV05136235X (ELSEVIER)S0923-5965(20)30136-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Wang, Ping verfasserin aut Thresholding binary coding for image forensics of weak sharpening 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics Elsevier Unsharp mask Elsevier Thresholding binary coding Elsevier Weak sharpening Elsevier Texture pattern mapping Elsevier Liu, Fenlin oth Yang, Chunfang oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:88 year:2020 pages:0 https://doi.org/10.1016/j.image.2020.115956 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 88 2020 0 |
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10.1016/j.image.2020.115956 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001132.pica (DE-627)ELV05136235X (ELSEVIER)S0923-5965(20)30136-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Wang, Ping verfasserin aut Thresholding binary coding for image forensics of weak sharpening 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics Elsevier Unsharp mask Elsevier Thresholding binary coding Elsevier Weak sharpening Elsevier Texture pattern mapping Elsevier Liu, Fenlin oth Yang, Chunfang oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:88 year:2020 pages:0 https://doi.org/10.1016/j.image.2020.115956 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 88 2020 0 |
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10.1016/j.image.2020.115956 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001132.pica (DE-627)ELV05136235X (ELSEVIER)S0923-5965(20)30136-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Wang, Ping verfasserin aut Thresholding binary coding for image forensics of weak sharpening 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics Elsevier Unsharp mask Elsevier Thresholding binary coding Elsevier Weak sharpening Elsevier Texture pattern mapping Elsevier Liu, Fenlin oth Yang, Chunfang oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:88 year:2020 pages:0 https://doi.org/10.1016/j.image.2020.115956 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 88 2020 0 |
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10.1016/j.image.2020.115956 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001132.pica (DE-627)ELV05136235X (ELSEVIER)S0923-5965(20)30136-3 DE-627 ger DE-627 rakwb eng 610 VZ 660 620 VZ 52.56 bkl Wang, Ping verfasserin aut Thresholding binary coding for image forensics of weak sharpening 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. Image forensics Elsevier Unsharp mask Elsevier Thresholding binary coding Elsevier Weak sharpening Elsevier Texture pattern mapping Elsevier Liu, Fenlin oth Yang, Chunfang oth Enthalten in Elsevier Zeng, C. ELSEVIER Effectiveness of continuous and pulsed ultrasound for the management of knee osteoarthritis: a systematic review and network meta-analysis 2014 theory, techniques & applications Amsterdam [u.a.] (DE-627)ELV017872103 volume:88 year:2020 pages:0 https://doi.org/10.1016/j.image.2020.115956 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_21 GBV_ILN_22 GBV_ILN_70 GBV_ILN_2002 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2015 52.56 Regenerative Energieformen alternative Energieformen VZ AR 88 2020 0 |
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Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. |
| abstractGer |
Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. |
| abstract_unstemmed |
Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations. |
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