Secure Information Embedding in Images with Hybrid Firefly Algorithm

• URL: http://arxiv.org/abs/2312.13519v1
• Date: Thu, 21 Dec 2023 01:50:02 GMT
• Title: Secure Information Embedding in Images with Hybrid Firefly Algorithm
• Authors: Sahil Nokhwal, Manoj Chandrasekharan, Ankit Chaudhary
• Abstract summary: This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image. The purpose of this search is to accomplish two main goals: increasing the host image's capacity and reducing distortion. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process.
• Score: 2.9182357325967145
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Various methods have been proposed to secure access to sensitive information over time, such as the many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a secret. Multiple techniques that are commonly discussed for such scenarios suffer from low capacity and high distortion in the output signal. This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image by employing the Hybrid Firefly algorithm (HFA) proposed to select the pixel arrangement. This algorithm combines two widely used optimization algorithms to improve their performance. The suggested methodology utilizes the HFA algorithm to conduct a search for optimal pixel placements in the spatial domain. The purpose of this search is to accomplish two main goals: increasing the host image's capacity and reducing distortion. Moreover, the proposed approach intends to reduce the time required for the embedding procedure. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process. The resultant embeddings exhibit robustness against steganalytic assaults, hence rendering the identification of the embedded data a formidable undertaking.

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