Improved Tampering Localization in Digital Image Forensics Based on Maximal Entropy Random Walk
In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random wal...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Korus, Pawel [verfasserIn] |
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Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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Enthalten in: IEEE signal processing letters - Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5, New York, NY, 19XX, 23(2016), 1, Seite 169-173 |
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| Übergeordnetes Werk: |
volume:23 ; year:2016 ; number:1 ; pages:169-173 |
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10.1109/LSP.2015.2507598 |
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| 520 | |a In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior. | ||
| 650 | 4 | |a Forensics | |
| 650 | 4 | |a tampering localization | |
| 650 | 4 | |a Organizations | |
| 650 | 4 | |a Detectors | |
| 650 | 4 | |a maximal entropy random walk | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a JPEG splicing | |
| 650 | 4 | |a Splicing | |
| 650 | 4 | |a visual saliency | |
| 650 | 4 | |a Digital image forensics | |
| 650 | 4 | |a first-digit features | |
| 650 | 4 | |a Entropy | |
| 650 | 4 | |a Digital images | |
| 700 | 1 | |a Huang, Jiwu |4 oth | |
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10.1109/LSP.2015.2507598 doi PQ20160430 (DE-627)OLC1971305448 (DE-599)GBVOLC1971305448 (PRQ)c1633-ef2301bc63f2a379a4c21b7c9a2dc24c8cf634a12b90e099cc26154e1c93a88d0 (KEY)02390256u20160000023000100169improvedtamperinglocalizationindigitalimageforensi DE-627 ger DE-627 rakwb eng 53.00 bkl Korus, Pawel verfasserin aut Improved Tampering Localization in Digital Image Forensics Based on Maximal Entropy Random Walk 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior. Forensics tampering localization Organizations Detectors maximal entropy random walk Visualization JPEG splicing Splicing visual saliency Digital image forensics first-digit features Entropy Digital images Huang, Jiwu oth Enthalten in Institute of Electrical and Electronics Engineers ; ID: gnd/1692-5 IEEE signal processing letters New York, NY, 19XX 23(2016), 1, Seite 169-173 (DE-627)182273075 (DE-600)916964-7 1070-9908 nnns volume:23 year:2016 number:1 pages:169-173 http://dx.doi.org/10.1109/LSP.2015.2507598 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7353154 http://search.proquest.com/docview/1750113149 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT 53.00 AVZ AR 23 2016 1 169-173 |
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Improved Tampering Localization in Digital Image Forensics Based on Maximal Entropy Random Walk |
| abstract |
In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior. |
| abstractGer |
In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior. |
| abstract_unstemmed |
In this paper we propose to use maximal entropy random walk on a graph for tampering localization in digital image forensics. Our approach serves as an additional post-processing step after conventional sliding-window analysis with a forensic detector. Strong localization property of this random walk will highlight important regions and attenuate the background - even for noisy response maps. Our evaluation shows that the proposed method can significantly outperform both the commonly used threshold-based decision, and the recently proposed optimization-based approach with a Markovian prior. |
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| title_short |
Improved Tampering Localization in Digital Image Forensics Based on Maximal Entropy Random Walk |
| url |
http://dx.doi.org/10.1109/LSP.2015.2507598 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7353154 http://search.proquest.com/docview/1750113149 |
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| author2 |
Huang, Jiwu |
| author2Str |
Huang, Jiwu |
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| doi_str |
10.1109/LSP.2015.2507598 |
| up_date |
2024-07-03T18:59:42.432Z |
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