New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization

El-Emam, Nameer N.

doi:10.1016/j.cose.2015.06.012

Cited by 39 publications

(16 citation statements)

References 45 publications

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“…On the other hand, a simple encryption algorithm will have faster encryption speed; however, it generally provides low-security protection. Different techniques that use multiple bit replacement per pixel were discussed by El-Emam [7], El-Emam, and Al-Diabat [8], and El-Emam and Qaddoum [9]. These techniques are based on a modified least significant bit (MLSB) algorithm to work against the steganalysis process.…”

Section: Related Workmentioning

confidence: 99%

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

Ahmad

El-Emam

AL-Azawi

2021

Int. j. commun. netw. inf. secur.

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Steganography algorithms have become a significant technique for preventing illegal users from obtaining secret data. In this paper, a deep hiding/extraction algorithm has been improved (IDHEA) to hide a secret message in colour images. The proposed algorithm has been applied to enhance the payload capacity and reduce the time complexity. Modified LSB (MLSB) is based on disseminating secret data randomly on a cover-image and has been proposed to replace a number of bits per byte (Nbpb), up to 4 bits, to increase payload capacity and make it difficult to access the hiding data. The number of levels of the IDHEA algorithm has been specified randomly; each level uses a colour image, and from one level to the next, the image size is expanded, where this algorithm starts with a small size of a cover-image and increases the size of the image gradually or suddenly at the next level, according to an enlargement ratio. Lossless image compression based on the run-length encoding algorithm and Gzip has been applied to enable the size of the data that is hiding at the next level, and data encryption using the Advanced Encryption Standard algorithm (AES) has been introduced at each level to enhance the security level. Thus, the effectiveness of the proposed IDHEA algorithm has been measured at the last level, and the performance of the proposed hiding algorithm has been checked by many statistical and visual measures in terms of the embedding capacity and imperceptibility. Comparisons between the proposed approach and previous work have been implemented; it appears that the intended approach is better than the previously modified LSB algorithms, and it works against visual and statistical attacks with excellent performance achieved by using the detection error (PE). Furthermore, the results confirmed that the stego-image with high imperceptibility has reached even a payload capacity that is large and replaces twelve bits per pixel (12-bpp). Moreover, testing is confirmed in that the proposed algorithm can embed secret data efficiently with better visual quality.

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Section: Related Workmentioning

confidence: 99%

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

Ahmad

El-Emam

AL-Azawi

2021

Int. j. commun. netw. inf. secur.

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“…Here, the capacity of the PSO algorithm is utilised to improve the concealment performance and the visual characteristics of the host image. In addition, [113] and [114] had suggested steganography techniques based on optimised adaptive neural networks for the purpose of removing noise. The application of this technique is utilised for the optimisation of the locations of particles where adaptive finite-element method (AFEM) is utilised to ascertain the estimated resolution of the second-order differential equation (SODE).…”

Section: Related Workmentioning

confidence: 99%

New Method of Image Steganography Based on Particle Swarm Optimization Algorithm in Spatial Domain for High Embedding Capacity

et al. 2019

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Steganography is a form of technology utilised to safeguard secret data during communication in addition to data repository. Numerous researchers have endeavoured to enhance the performance of steganography techniques through the development of an effective algorithm for the selection of the optimal pixel location within the host image for the concealment of secret bits, for the enhancement of the embedding capacity of the secret data, and for maintaining the visual quality of the host image (stego image) in an accepted rate after the concealment of the secret data. Therefore, steganography is perceived as a challenging task. Thus, the current study proposes a new technique for image steganography based on particle swarm optimisation (PSO) algorithm by using pixel selection for the concealment of a secret image in spatial domain, for the purpose of high embedment capacity. The stego possesses a high level of resistance against a steganalytic attack due to the security provided via image steganography. The function of PSO algorithm is to choose an optimal pixel in grey scale host image for the concealment of secret bits, as the PSO has the ability to achieve an efficient fitness calculation that depends on the cost matrix by dividing the host and secret images into four parts. First of all, the secret bits are modified, which are then embedded within the host image. Several locations in the host image are determined through the order of scanning the host pixels and starting point of the scanning for better least significant bits LSBs of each pixel. The PSO algorithm was utilised to ascertain the ideal initiating point and scanning order. Experimental results show that (1) the average peak signal to noise ratio PSNR value in the benchmark technique based on genetic algorithm for five standard stego images is 45.13%, whereas the result obtained from the recommended technique is 56.60%. (2) The proposed technique has an advantage over the benchmark with a percentage of 33.34%, which encompasses all associated issues within the checklist scenario. Therefore, the performance of the recommended technique is superior over existing techniques.

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“…[11] developed an intelligent reversible watermarking approach for medical images by using GA to make an optimal tradeoff between imperceptibility and payload through effective selection of threshold. Modified Particle Swarm Optimization algorithm (MPSO) was introduced by (EL-Emam, 2015 [12]) used to improve the quality of stego-image by deriving an optimal change on the lower nibbles of each byte at sego-image. Fan Zhang et al [13] (2008) proposed a new method of information-embedding capacity bound's analysis that is based on the neural network theories of attractors and attraction basins.…”

Section: Introductionmentioning

confidence: 99%

Improved Steganographic Security by Applying an Irregular Image Segmentation and Hybrid Adaptive Neural Networks With Modified Ant Colony Optimization

El-Emam¹,

Qaddoum²

2015

IJNSA

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In this paper, a new steganography algorithm has been suggested to enforce the security of data hiding and to increase the amount of payloads. This algorithm is based on four safety layers; the first safety layer has been initiated through compression and an encryption of a confidential message using a set partition in hierarchical trees (SPIHT) and advanced encryption standard (AES) mechanisms respectively. An irregular image segmentation algorithm (IIS) on a cover-image (Ic) has been constructed successfully in the second safety layer, and it is based on the adaptive reallocation segments' edges (ARSE) by applying an adaptive finite-element method (AFEM) to find the numerical solution of the proposed partial differential equation (PDE). An intelligent computing technique using a hybrid adaptive neural network with a modified ant colony optimizer (ANN_MACO) has been proposed in the third safety layer to construct a learning system. This system accepts entry using support vector machine (SVM) to generate input patterns as features of byte attributes and produces new features to modify a cover-image.The significant innovation of the proposed novel steganography algorithm is applied efficiently on the forth safety layer which is more robust for hiding a large amount of confidential message reach to six bits per pixel (bpp) into color images. The new approach of hiding algorithm works against statistical and visual attacks with high imperceptible of hiding data into stego-images (Is). The experimental results are discussed and compared with the previous steganography algorithms; it demonstrates that the proposed algorithm has a significant improvement on the effect of the security level of steganography by making an arduous task of retrieving embedded confidential message from color images.

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New data-hiding algorithm based on adaptive neural networks with modified particle swarm optimization

Cited by 39 publications

References 45 publications

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

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

New Method of Image Steganography Based on Particle Swarm Optimization Algorithm in Spatial Domain for High Embedding Capacity

Improved Steganographic Security by Applying an Irregular Image Segmentation and Hybrid Adaptive Neural Networks With Modified Ant Colony Optimization

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