SABMIS: Sparse approximation based blind multi-image steganography scheme
Rohit Agrawal, Kapil Ahuja, Marc C. Steinbach, Thomas Wick

TL;DR
This paper introduces SABMIS, a novel steganography scheme that effectively hides multiple secret images into a cover image with high capacity, maintaining visual quality and resistance to attacks, using sparse approximation and ADMM optimization.
Contribution
The paper presents a new sparse approximation based blind multi-image steganography scheme with enhanced capacity, robustness, and minimal quality deterioration, outperforming existing methods.
Findings
Achieves up to 8 bpp embedding capacity for four secret images.
Outperforms existing methods in hiding two and three secret images.
Demonstrates robustness and efficiency in real-life applications.
Abstract
We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using ADMM to solve the LASSO formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Medical Image Segmentation Techniques · Image and Signal Denoising Methods
