Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4818
Title: CSIS: Compressed sensing-based enhanced-embedding capacity image steganography scheme
Authors: Agrawal, Rohit
Ahuja, Kapil
Keywords: Compressed sensing;Cryptography;Embeddings;Image compression;Steganography;Embedding capacity;Encrypted data;Grey scale images;Image steganography;Linear measurements;Optimization problems;Steganalysis attacks;Visual qualities;Image enhancement
Issue Date: 2021
Publisher: John Wiley and Sons Inc
Citation: Agrawal, R., & Ahuja, K. (2021). CSIS: Compressed sensing-based enhanced-embedding capacity image steganography scheme. IET Image Processing, 15(9), 1909-1925. doi:10.1049/ipr2.12161
Abstract: Image steganography plays a vital role in securing secret data by embedding it in the cover images. Usually, these images are communicated in a compressed format. Existing techniques achieve this but have low embedding capacity. Hence, the goal here is to enhance the embedding capacity while preserving the visual quality of the stego-image. It is also intended to ensure that the scheme is resistant to steganalysis attacks. This paper proposes a compressed sensing image steganography (CSIS) scheme to achieve these goals. In CSIS, the cover image is sparsified block-wise, linear measurements are obtained, and then permissible measurements are selected. Next, the secret data is encrypted, and 2 bits of this encrypted data are embedded into each permissible measurement. For the reconstruction of the stego-image, ADMM and LASSO are used for the resultant optimization problem. Experiments are performed on several standard greyscale images and a colour image. Higher embedding capacity, 1.53 times more compared to the most recent scheme, is achieved. An average of 37.92 dB PSNR value, and average values close to 1 for both the mean SSIM index and the NCC coefficients are obtained, which is considered good. These metrics show that CSIS substantially outperforms existing similar steganography schemes. © 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
URI: https://doi.org/10.1049/ipr2.12161
https://dspace.iiti.ac.in/handle/123456789/4818
ISSN: 1751-9659
Type of Material: Journal Article
Appears in Collections:Department of Computer Science and Engineering

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