Secret Data Hiding Scheme with Least Significant Bit Matching Revisited Image Steganography based on Novel E-Coli Bacterial Foraging Optimization Model

Steganography is the act of hiding an information inside another message (i.e., video or image) in such a way that to be detected only by its authorized receiver, with intermediates unable to identify the secret information. The computation complexity involved during embedding the hidden data in depth maps using 3D images was high while performing embedding and reversing operations. Specifically, we present a four step model for reducing the computational complexity and improve robustness for effectual secret data hiding. The first step in E-coli BFO model is that the Bacteria (i.e.,) secret information to be embedded is randomly distributed in nutrients map (i.e.,) with the embedding image. The E-coli BFO model evaluates the fitness value for information to be embedded. The second step, swarming operation is carried out in the E-coli BFO cells to locate the center where the secret information has to be embedded. Followed by this, the third step performs the elimination and dispersal in E-coli BFO model that efficiently removes the unauthorized person who tries to extract the secret information. Finally, the fourth step, reproduction work is carried out to reproduce the secret information to the correct destination for significant information extraction. Simulations with MATLAB software were used to evaluate the performance of LSMBR in terms of computational complexity, image quality level and average bit error rate level and compared it with two other existing state-of-the-art methods. The results obtained through simulation demonstrate the effective performance of the proposed method in terms of robustness and computational complexity.