Multibit least significant bit matching: a novel approach to image steganography

Almabrok Essa; Ian Wu

doi:10.1117/12.3014037

7 June 2024 Multibit least significant bit matching: a novel approach to image steganography

Almabrok Essa, Ian Wu

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330E (2024) https://doi.org/10.1117/12.3014037
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Steganography has a large range of potential applications, particularly with the advent of the internet and intellectual property concerns. A particular technique, Least Significant Bit (LSB) Steganography, is commonly used for image-in-image steganography. However, LSB steganography is a bit weak against steganalysis attacks that aim to detect the presence of embedded data. This weakness has been improved upon in Least Significant Bit Matching (LSBM) Steganography, which attempts to preserve the underlying structure of the image by maintaining a similar number of 1s and 0s as in the original image. However, standard LSBM steganography is only able to encode 1 bit of information per channel per pixel, which limits the information that could be embedded. To this end, a novel multibit approach to LSBM steganography is proposed named Multibit Least Significant Bit Matching (MLSBM) Steganography. The MLSBM approach preserves the underlying structure of the image, while allowing multiple bits to be encoded of each pixel in each channel. In addition, when the proposed MLSBM technique is used to embed high number of bits, it significantly reduces the visual perceptibility of the embedding when it is compared with the other embedding techniques for the same number of bits.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Almabrok Essa and Ian Wu "Multibit least significant bit matching: a novel approach to image steganography", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330E (7 June 2024); https://doi.org/10.1117/12.3014037

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