Image steganography based on pixel ranking and Particle Swarm Optimization

Nickfarjam, Ali Mohammad; Azimifar, Zohreh

doi:10.1109/aisp.2012.6313773

Cited by 16 publications

(7 citation statements)

References 9 publications

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“…This approach quantizes a table of size 16*16 instead of the commonly used size 8*8 at the most JPEG compression to reach a high capacity, while PSO is applied to obtain good quality with an optimal substitution matrix to transform the secret data into the best fit in a cover image before the embedding process. A novel approach for image steganography that takes advantage of Particle Swarm Optimization (PSO) and the least bits (LSBs) replacement has been proposed by (Nickfarjam and Azimifar, 2012); this technique was based on hiding the most significant bits (MSBs) of the secret image pixels in the LSBs of a host image to find the best pixel to embed. Gerami et al (2012) proposed a method that utilizes particle swarm optimization (PSO) for finding the best pixel locations, and then, the secret image is transformed into a new secret image.…”

Section: Introductionmentioning

confidence: 99%

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

El-Emam

2015

Computers & Security

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Section: Introductionmentioning

confidence: 99%

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

El-Emam

2015

Computers & Security

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“…Nickfarjam and Azimifar propose a scheme for image steganography that uses Particle Swarm Optimization (PSO) and LSB replacement [35]. The process depends on concealing Most Significant Bits (MSBs) of pixels in the secret data (e.g, image) in the LSBs of the carrier image.…”

Section: Related Work On Evolutionary Steganographymentioning

confidence: 99%

An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms

et al. 2019

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In stenography, embedding data within an image has a trade-off between image quality and embedding capacity. Specifically, the more data are concealed within a carrier image, the further distortion the image suffers, causing a decline in the resultant stego image quality. Embedding high capacity of data into an image while preserving the quality of the carrier image can be seen as an optimization problem. In this paper, we propose a novel spatial steganography scheme using genetic algorithms (GAs). Our scheme utilizes new operations to increase least significant bits (LSB) matching between the carrier and the stego image which results in increased embedding capacity and reduced distortion. These operations are optimized pixel scanning in vertical and horizontal directions, circular shifting, flipping secret bits and secret data transposing. We formulate a general GA-based steganography model to search for the optimum solutions. Finally, we use LSB substitution for data embedding. We conduct extensive experimental testing of the proposed scheme and compare it to the state-of-art steganography schemes. The proposed scheme outperforms the relevant GA-based steganography methodologies.

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“…Its protection depends on the privacy of the algorithm. As a result, it is known as a less reliable approach [8,9]. Another way to hide information is to hide confidential data, which uses one key for all operations (embedding and extraction).…”

Section: Introductionmentioning

confidence: 99%

DNA Computing Using Cryptographic and Steganographic Strategies

Adithya¹,

Santhi²

2021

Data Integrity and Quality

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Information protection and secrecy are major concerns, especially regarding the internet’s rapid growth and widespread usage. Unauthorized database access is becoming more common and is being combated using a variety of encrypted communication methods, such as encryption and data hiding. DNA cryptography and steganography are used as carriers by utilizing the bio-molecular computing properties that have become more common in recent years. This study examines recently published DNA steganography algorithms, which use DNA to encrypt confidential data transmitted through an insecure communication channel. Several DNA-based steganography strategies will be addressed, with a focus on the algorithm’s advantages and drawbacks. Probability cracking, blindness, double layer of security, and other considerations are used to compare steganography algorithms. This research would help and create more effective and accurate DNA steganography strategies in the future.

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Image steganography based on pixel ranking and Particle Swarm Optimization

Cited by 16 publications

References 9 publications

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

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

An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms

DNA Computing Using Cryptographic and Steganographic Strategies

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