Blind Median Filtering Detection Using Auto-Regressive Model and Markov Chain Article Swipe
Anjie Peng
,
Yu Gao
,
Hui Zeng
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5430/ijrc.v1n1p32
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5430/ijrc.v1n1p32
Establishing the processing history of an image is important for robot vision. In this paper, an improved method for median filtering detection is proposed. That is, detect whether an image has been processed by median filtering. First, we analyze the statistical properties of median filtering residual and find that it is suitable for exposing fingerprints of median filtering. Then, the new feature set on median filtering residual is constructed by incorporating transition probability matrices of Markov chain with coefficients of auto-regressive model. A dimensionality reduction method is developed to lower the feature dimensionality. The final feature set is fed into support vector machines to construct a detector. Due to the distinction property of median filtering residual as well as compensated effect between transition probability and auto-regressive model, experimental results on large image database demonstrate that the proposed method is effectively in median filtering detection, even for images with heavy JPEG compression or at a low resolution. The performance of proposed detector outperforms prior arts. Additionally, the proposed method demonstrates good generalization ability.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5430/ijrc.v1n1p32
- https://www.sciedupress.com/journal/index.php/ijrc/article/download/13857/8652
- OA Status
- diamond
- Cited By
- 2
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2886903739
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2886903739Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5430/ijrc.v1n1p32Digital Object Identifier
- Title
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Blind Median Filtering Detection Using Auto-Regressive Model and Markov ChainWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-10Full publication date if available
- Authors
-
Anjie Peng, Yu Gao, Hui ZengList of authors in order
- Landing page
-
https://doi.org/10.5430/ijrc.v1n1p32Publisher landing page
- PDF URL
-
https://www.sciedupress.com/journal/index.php/ijrc/article/download/13857/8652Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://www.sciedupress.com/journal/index.php/ijrc/article/download/13857/8652Direct OA link when available
- Concepts
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Residual, Artificial intelligence, Pattern recognition (psychology), Markov chain, Median filter, Computer science, Feature (linguistics), Mathematics, Image processing, Computer vision, Image (mathematics), Algorithm, Machine learning, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.median | 19, 34, 43, 56, 64, 113, 141 |
| abstract_inverted_index.method | 17, 85, 137, 167 |
| abstract_inverted_index.model, | 126 |
| abstract_inverted_index.model. | 81 |
| abstract_inverted_index.paper, | 14 |
| abstract_inverted_index.vector | 101 |
| abstract_inverted_index.analyze | 38 |
| abstract_inverted_index.between | 121 |
| abstract_inverted_index.feature | 61, 91, 95 |
| abstract_inverted_index.history | 3 |
| abstract_inverted_index.results | 128 |
| abstract_inverted_index.support | 100 |
| abstract_inverted_index.vision. | 11 |
| abstract_inverted_index.whether | 27 |
| abstract_inverted_index.ability. | 171 |
| abstract_inverted_index.database | 132 |
| abstract_inverted_index.detector | 160 |
| abstract_inverted_index.exposing | 53 |
| abstract_inverted_index.improved | 16 |
| abstract_inverted_index.machines | 102 |
| abstract_inverted_index.matrices | 73 |
| abstract_inverted_index.property | 111 |
| abstract_inverted_index.proposed | 136, 159, 166 |
| abstract_inverted_index.residual | 45, 66, 115 |
| abstract_inverted_index.suitable | 51 |
| abstract_inverted_index.construct | 104 |
| abstract_inverted_index.detection | 21 |
| abstract_inverted_index.detector. | 106 |
| abstract_inverted_index.developed | 87 |
| abstract_inverted_index.filtering | 20, 44, 65, 114, 142 |
| abstract_inverted_index.important | 8 |
| abstract_inverted_index.processed | 32 |
| abstract_inverted_index.proposed. | 23 |
| abstract_inverted_index.reduction | 84 |
| abstract_inverted_index.detection, | 143 |
| abstract_inverted_index.filtering. | 35, 57 |
| abstract_inverted_index.processing | 2 |
| abstract_inverted_index.properties | 41 |
| abstract_inverted_index.transition | 71, 122 |
| abstract_inverted_index.compensated | 119 |
| abstract_inverted_index.compression | 150 |
| abstract_inverted_index.constructed | 68 |
| abstract_inverted_index.demonstrate | 133 |
| abstract_inverted_index.distinction | 110 |
| abstract_inverted_index.effectively | 139 |
| abstract_inverted_index.outperforms | 161 |
| abstract_inverted_index.performance | 157 |
| abstract_inverted_index.probability | 72, 123 |
| abstract_inverted_index.resolution. | 155 |
| abstract_inverted_index.statistical | 40 |
| abstract_inverted_index.Establishing | 0 |
| abstract_inverted_index.coefficients | 78 |
| abstract_inverted_index.demonstrates | 168 |
| abstract_inverted_index.experimental | 127 |
| abstract_inverted_index.fingerprints | 54 |
| abstract_inverted_index.Additionally, | 164 |
| abstract_inverted_index.incorporating | 70 |
| abstract_inverted_index.dimensionality | 83 |
| abstract_inverted_index.generalization | 170 |
| abstract_inverted_index.auto-regressive | 80, 125 |
| abstract_inverted_index.dimensionality. | 92 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.44953755 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |