A New Image Steganography Algorithm Based on MLSB Method with Random Pixels Selection

Al-Shatanawi, Odai M.; N.El.Emam, Nameer

doi:10.5121/ijnsa.2015.7203

Cited by 16 publications

(11 citation statements)

References 25 publications

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“…Results with ANN_MACO improve the quality without ANN_MACO and the earlier work (El-Emam, 2013, [17]), and (Al-Shatanawi, 2015 [18]) respectively. Moreover, results are showing that PSNR of Lena's compression in F16's stegoimage has a best quality with ANN_ MACO, but it is better than [17] with 1.7 dB, and better than [18] with 2.96 dB, whereas, Tiffany's compression in Baboon stego-image has a worst quality with ANN_ MACO, but it is better than [17] with 0.88 dB, and better than [18] with 4.76 dB. Table 2 shows the comparison between the experimental results of the proposed hiding algorithm with/without ANN_MACO and the algorithm in [17].…”

Section: The Amount Of Payloads Vs Image Distortionmentioning

confidence: 56%

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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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Section: The Amount Of Payloads Vs Image Distortionmentioning

confidence: 56%

“…Table 1. PSNR(dB) results of Is on a color plane between El-Emam (2013) algorithm [17], Al-Shatanawi, (2015) [18], and the proposed algorithm…”

Section: The Amount Of Payloads Vs Image Distortionmentioning

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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“…Neil F. Johnson [20] proposed a method that sharp areas can hide large amount of data if it is applied at edge image and it cannot hide more data when it works at smooth images. Odai et al [1] proposed an algorithm based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially.…”

Section: Jpeg2000 / Jpeg2kmentioning

confidence: 99%

Image Steganography: Critical Findings through Some Novel Techniques

Nabi,

Afzal

2020

IJITEE

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Word 'Steganography' is originated from Greekand has been used in several forms for 2500 years which is a art of hiding confidential data in any digital manner in such a way that no one can concealed it. It has found practise in various section like in military, government, diplomatic, medical, personal Interest) is highlighted. In addition, different types of Image file used in image steganography and performance parameter are discuss well. and intelligence agencies. This survey paper put the light on the basic of image steganography along with its different techniques and sub-techniques. Cover selection with recent trend ROI (region Of

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“…Apply the inverse of the Dev.-PSO method to a stegano image to extract the secret image. Odai et al [12] presented the Modified LSB method for image steganography with random pixel selection. The hybrid combination of Modified LSB and image segmentation is used before embedding the secret images randomly.…”

Section: Related Workmentioning

confidence: 99%

Design of an Efficient Steganography Model using Lifting based DWT and Modified-LSB Method on FPGA

Mahesh¹,

K.B²

2019

IJACSA

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The data transmission with information hiding is a challenging task in today world. To protect the secret data or image from attackers, the steganography techniques are essential. The steganography is a process of hiding the information from one channel to another in data communication. In this research work, Design of an Efficient Steganography Model using Lifting Based DWT and Modified-LSB Method on FPGA is proposed. The stegano module includes DWT (Discrete Wavelet Transformation) with lifting scheme for the cover image and encryption with Bit mapping for a secret image, an embedded module using Modified Least Significant Bit (MLSB) Method, and Inverse DWT to generate the stegano image. The recovery module includes DWT, decoding module with pixel extraction and bit retrievals, and decryption to generate the recovered secret image. The steganography model is designed using Verilog-HDL on Xilinx platform and implemented with Artix-7 Field Programmable Gate Array (FPGA). The hardware resource constraints like Area, time, and power utilization of the proposed model results are tabulated. The performance analysis of the work is evaluated using Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) Ratio for a different cover and secret images with better quality. The proposed steganography model operates at high speed, which improves communication performance.

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A New Image Steganography Algorithm Based on MLSB Method with Random Pixels Selection

Abstract: ABSTRACT

Cited by 16 publications

References 25 publications

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

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

Image Steganography: Critical Findings through Some Novel Techniques

Design of an Efficient Steganography Model using Lifting based DWT and Modified-LSB Method on FPGA

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