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Superpixel based robust reversible data hiding scheme exploiting Arnold transform with DCT and CA
- Source :
- Journal of King Saud University - Computer and Information Sciences. 34:4402-4420
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- The emergence of data hiding schemes was to provide multimedia security besides the sharing of information in a confidential manner. Since then, various techniques have cropped up to meet the objective of secured data communication using multimedia documents. In this paper, a novel data hiding approach has been designed to exploit the superpixel of the image to identify the nature of the blocks for embedding data using spatial and transform domain techniques such as Cellular Automata (CA) and Discrete Cosine Transform (DCT) respectively. In addition, an Arnold transform is used to provide a blanket of security through a random selection of blocks via sharable keys. The proposed technique obtains YCbCr color model of an RGB image to use superpixel on only the Y color component. Thereafter, the acquired labeled image of superpixel is partitioned into ( 8 × 8 ) blocks which are to be categorised as homogeneous and heterogeneous blocks. Based on the categorization, DCT and CA are applied for the embedding of the secret data in the Cb and Cr color components. The experimental results and its comparison with state-of-the-art schemes uphold the effectiveness of the demonstrated scheme. Various analysis have been conducted to showcase the degree of robustness, imperceptibility, and visual quality attained by the proposed scheme.
- Subjects :
- General Computer Science
Exploit
Computer science
business.industry
020206 networking & telecommunications
Pattern recognition
YCbCr
02 engineering and technology
Cellular automaton
Color model
Robustness (computer science)
Information hiding
0202 electrical engineering, electronic engineering, information engineering
Discrete cosine transform
Embedding
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 13191578
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Journal of King Saud University - Computer and Information Sciences
- Accession number :
- edsair.doi...........bc14a221624c9829bc6cd72380f26881
- Full Text :
- https://doi.org/10.1016/j.jksuci.2020.09.014