Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks Article Swipe
Donghui Hu
,
Qiang Shen
,
Shengnan Zhou
,
Xueliang Liu
,
Yuqi Fan
,
Lina Wang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/2314860
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1155/2017/2314860
Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2017/2314860
- http://downloads.hindawi.com/journals/scn/2017/2314860.pdf
- OA Status
- hybrid
- Cited By
- 13
- References
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2768061508
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2768061508Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2017/2314860Digital Object Identifier
- Title
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Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-01-01Full publication date if available
- Authors
-
Donghui Hu, Qiang Shen, Shengnan Zhou, Xueliang Liu, Yuqi Fan, Lina WangList of authors in order
- Landing page
-
https://doi.org/10.1155/2017/2314860Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/scn/2017/2314860.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/scn/2017/2314860.pdfDirect OA link when available
- Concepts
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Steganalysis, Computer science, Convolutional neural network, Steganography, Artificial intelligence, Pattern recognition (psychology), Embedding, Feature selection, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
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2023: 4, 2022: 2, 2021: 2, 2020: 1, 2019: 2Per-year citation counts (last 5 years)
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11Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Hindawi Publishing Corporation |
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| primary_location.source.host_organization_lineage_names | Hindawi Publishing Corporation |
| primary_location.license | cc-by |
| primary_location.pdf_url | http://downloads.hindawi.com/journals/scn/2017/2314860.pdf |
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| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.is_published | True |
| primary_location.raw_source_name | Security and Communication Networks |
| primary_location.landing_page_url | https://doi.org/10.1155/2017/2314860 |
| publication_date | 2017-01-01 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2124890704, https://openalex.org/W2154026545, https://openalex.org/W2170598445, https://openalex.org/W2134527668, https://openalex.org/W2009130368, https://openalex.org/W2277839806, https://openalex.org/W2112796928, https://openalex.org/W2322622188, https://openalex.org/W2739185626, https://openalex.org/W22271197, https://openalex.org/W2124664712 |
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