Modified Multi-Level Steganography to Enhance Data Security

Abstract

Data-hiding using steganography algorithm becomes an important technique to prevent unauthorized users to have access to a secret data. In this paper, steganography algorithm has been constructed to hide a secret data in a gray and a color images, this algorithm is named deep hiding/extraction algorithm (DHEA) to modify multi-level steganography (MLS). The suggested hiding algorithm is based on modified least significant bit (MDLSB) to scatter data in a cover-image and it utilizes a number of levels; where each level perform hiding data on a gray image except the last level that applies a color image to keep secret data. Furthermore, proper randomization approach with two layers is implemented; the first layer uses random pixels selection for hiding a secret data at each level, while the second layer implements at the last level to move randomly from segment to the others. In addition, the proposed hiding algorithm implements an effective lossless image compression using DEFLATE algorithm to make it possible to hide data into a next level. Dynamic encryption algorithm based on Advanced Encryption Standard (AES) is applied at each level by changing cipher keys (Ck) from level to the next, this approach has been applied to increase the security and working against attackers. Soft computing using a meta-heuristic approach based on artificial bee colony (ABC) algorithm has been introduced to achieve smoothing on pixels of stego-image, this approach is effective to reduce the noise caused by a hidden large amount of data and to increase a stego-image quality on the last level. The experimental result demonstrates the effectiveness of the proposed algorithm with bee colony DHA-ABC to show high-performing to hide a large amount of data up to four bits per pixel (bpp) with high security in terms of hard extraction of a secret message and noise reduction of the stego-image. Moreover, using deep hiding with unlimited levels is promising to confuse attackers and to compress a deep sequence of images into one image.

New deep data hiding and extraction algorithm using multi-channel with multi-level to improve data security and payload capacity

“…The proposed hiding algorithm is based on bit replacement using NMLSB. This algorithm is compared with previous works such as the MDLSB algorithm by Elshare & EL-Emam (2018) , the MLSB algorithm by Ahmad, El-Emam & AL-Azawi (2021) , reversible data hiding by Ou et al (2015) , and the noise-like binary image blocks by Li, Li & Yang (2013) , see Table 1 .…”

“…According to these metrics’ results, different payload capacities on three stego-images (from the standard-test-images) are applied to show the system performance. Furthermore, the proposed approach results have been compared with the previous works ( Elshare & EL-Emam, 2018 ; Ahmad, El-Emam & AL-Azawi, 2021 ; Ou et al, 2015 ; Li, Li & Yang, 2013 ). As a result, the average PSNR for the three images using the proposed hiding algorithm is better than the results of the previous algorithms for the same images with the same data hidden capacity.…”

“…where μ Ic and μ Is metrics represent the average of the cover and stego images, respectively, σIcIs metric represents the covariance between the cover and the stego images, and ( ) represents the variance between the cover and the stego images ( Elshare & EL-Emam, 2018 ).…”

“… Elshare & EL-Emam (2018) proposed a deep data hiding and extraction algorithm (DHEA) to hide extensive secret data in multi-level color images. This algorithm improves the multi-level steganography technique (MLS) and enhances the security and payload capacity.…”

“…Furthermore, this technique is based on deep data hiding and extraction algorithm working on a multi-channel (MCDHEA). This algorithm is the extended version of the old techniques such as multi-level steganography (MLS) ( Mahdi et al, 2019 ), deep data hiding and extraction algorithm (DHEA) ( Elshare & EL-Emam, 2018 ), and improved deep data hiding and extraction (IDHEA) ( Ahmad, El-Emam & AL-Azawi, 2021 ). The proposed algorithm (MCDHEA) has been implemented in two phases.…”