Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Ahmad, Marwa; El-Emam, Nameer N.; AL-Azawi, Ali F.

doi:10.54039/ijcnis.v13i3.4759

Cited by 2 publications

(10 citation statements)

References 16 publications

(25 reference statements)

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“…Simulation findings reveal that compared to current protocols, the proposed protocol improves network longevity and performance.U. Srilakshmi [17] proposed Genetic Algorithm with Hill Climbing (GAHC). Finding the optimum multipath route using a GA-Hill Climbing algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

Prasad¹,

Vuyyuru²,

Sushma³

et al. 2022

IJRITCC

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A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack.

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Section: Related Workmentioning

confidence: 99%

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

Prasad¹,

Vuyyuru²,

Sushma³

et al. 2022

IJRITCC

View full text Add to dashboard Cite

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“… Ahmad, El-Emam & AL-Azawi (2021) constructed an improved deep data hiding and extraction algorithm (IDHEA) working on color images. This algorithm develops the hiding process by enhancing the security level and payload capacity by using a small size of the cover-image at the first level and gradually increasing the size of the cover-image from one level to the next according to the enlargement ratio.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed steganography algorithm uses a multi-channel deep data hiding and extraction (MCDHEA) technique. This technique is applied to improve the previous work based on the (IDHEA) algorithm ( Ahmad, El-Emam & AL-Azawi, 2021 ) by using two types of channels. The main channel is the first type, whereas the sub-channel is the second type.…”

Section: Objectives and Motivationmentioning

confidence: 99%

“…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.…”

Section: The Suggested Hiding and Extraction Algorithmmentioning

confidence: 99%

“…The MCDHA is the modified version of the previous work ( Ahmad, El-Emam & AL-Azawi, 2021 ) by dividing a secret message SM into n blocks {SM1, SM2, …, SMn}, and distributing these blocks on a multi-channel. Where SMi is the i th secret message hidden into a cover image of the selected channel index and level number at this channel randomly.…”

Section: The Suggested Hiding and Extraction Algorithmmentioning

confidence: 99%

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

Hardan¹,

Alawneh²,

El-Emam³

2022

PeerJ Computer Science

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The main challenge in steganography algorithms is balancing between the size of the secret message (SM) that is embedded in the cover image (CI) and the quality of the stego-image (SI). This manuscript proposes a new steganography algorithm to hide a large amount of secret messages in cover images with a high degree of non-perception in the resulting images. The proposed algorithm applied a multi-channel deep data hiding and extraction algorithm (MCDHEA) based on a modified multi-level steganography (MLS) approach. This approach used a new modification of the least significant bits (NMLSB) to make it hard to extract a secret message from attackers. The secret message was distributed among n-blocks; each block was hidden into a sub-channel that included multi-level hiding and flows into the main channel. Different grayscale images were used through the levels of each sub-channel and using the color image in the last level of the main channel. The image size of the multi-level was expanded from one level to the next level, and at each level, lossless image compression using the Huffman coding algorithm was applied to enable the size of the data hiding at the next level. In addition, the encryption of secret messages and intermediate cover images based on the XOR encryption algorithm is applied before the hiding process. Finally, the number of bits to be replaced at each level for both sub and main channels was four bits per byte except at the last level of the main channel based on a new approach using a non-uniform number of bits replacements. This algorithm’s performance was evaluated using various measures. The results show that the proposed technique is effective and better than the previous works concerning imperceptibility and robustness. Furthermore, the results show that the maximum peak signal-to-noise ratio (PSNR) of 61.2 dB for the payload of 18,750 bytes, the maximum video information fidelity (VIF) of 0.95 for the payload of 19,660 bytes, and the maximum structural similarity index measure (SSIM) of 0.999 for the payload of 294,912 bytes.

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Improved Deep Hiding/Extraction Algorithm to Enhance the Payload Capacity and Security Level of Hidden Information

Cited by 2 publications

References 16 publications

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

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

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