Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography Article Swipe
Zihan Chen
,
Tianrui Liu
,
Junjie Huang
,
Wentao Zhao
,
Xing Bi
,
Meng Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.08987
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.08987
The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information among multiple images will become increasingly severe when the number of secret images becomes larger, thus restrict the development of very large capacity image steganography. In this paper, we propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes very large capacity image steganography with high quality by concealing a single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling (IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works for downscaling the single mosaic secret image form by spatially splicing the multiple secret images, and the IIH module then conceal this mosaic image under the cover image. The proposed InvMIHNet successfully conceal and reveal up to 16 secret images with a small number of parameters and memory consumption. Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms state-of-the-art methods in terms of both the imperceptibility of stego image and recover accuracy of secret image.
Related Topics
Concepts
Steganography
Image (mathematics)
Computer science
Concatenation (mathematics)
Cover (algebra)
Digital image
Invertible matrix
Artificial intelligence
Information hiding
Computer vision
Theoretical computer science
Mathematics
Image processing
Arithmetic
Pure mathematics
Mechanical engineering
Engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.08987
- https://arxiv.org/pdf/2309.08987
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386875177
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386875177Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.08987Digital Object Identifier
- Title
-
Invertible Mosaic Image Hiding Network for Very Large Capacity Image SteganographyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-16Full publication date if available
- Authors
-
Zihan Chen, Tianrui Liu, Junjie Huang, Wentao Zhao, Xing Bi, Meng WangList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.08987Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.08987Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.08987Direct OA link when available
- Concepts
-
Steganography, Image (mathematics), Computer science, Concatenation (mathematics), Cover (algebra), Digital image, Invertible matrix, Artificial intelligence, Information hiding, Computer vision, Theoretical computer science, Mathematics, Image processing, Arithmetic, Pure mathematics, Mechanical engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.module. | 93 |
| abstract_inverted_index.propose | 53 |
| abstract_inverted_index.quality | 70 |
| abstract_inverted_index.recover | 169 |
| abstract_inverted_index.accuracy | 170 |
| abstract_inverted_index.capacity | 46, 65 |
| abstract_inverted_index.consists | 79 |
| abstract_inverted_index.existing | 1 |
| abstract_inverted_index.multiple | 15, 25, 110 |
| abstract_inverted_index.proposed | 127 |
| abstract_inverted_index.realizes | 62 |
| abstract_inverted_index.restrict | 40 |
| abstract_inverted_index.splicing | 108 |
| abstract_inverted_index.Extensive | 147 |
| abstract_inverted_index.InvMIHNet | 78, 128, 155 |
| abstract_inverted_index.Rescaling | 84 |
| abstract_inverted_index.spatially | 107 |
| abstract_inverted_index.Invertible | 55, 82, 89 |
| abstract_inverted_index.concealing | 72 |
| abstract_inverted_index.parameters | 143 |
| abstract_inverted_index.(InvMIHNet) | 60 |
| abstract_inverted_index.development | 42 |
| abstract_inverted_index.downscaling | 99 |
| abstract_inverted_index.experiments | 148 |
| abstract_inverted_index.information | 23 |
| abstract_inverted_index.outperforms | 156 |
| abstract_inverted_index.ImageNet-1K, | 150 |
| abstract_inverted_index.consumption. | 146 |
| abstract_inverted_index.increasingly | 29 |
| abstract_inverted_index.interference | 21 |
| abstract_inverted_index.sequentially | 6 |
| abstract_inverted_index.successfully | 129 |
| abstract_inverted_index.concatenation | 13 |
| abstract_inverted_index.steganography | 3, 67 |
| abstract_inverted_index.steganography. | 48 |
| abstract_inverted_index.imperceptibility | 164 |
| abstract_inverted_index.state-of-the-art | 157 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |