Image hiding by optimal LSB substitution and genetic algorithm

Wang, Ran-Zan; Lin, Chia-Li; Lin, Ja‐Chen

doi:10.1016/s0031-3203(00)00015-7

Cited by 618 publications

(291 citation statements)

References 9 publications

Supporting

Mentioning

263

Contrasting

Unclassified

Order By: Relevance

“…P X is 10 after the data embedding. The adjustment phase cannot be executed successfully because NPE (12 = 22 10 -, or 4 22 26 = -) and PE fall in different ranges of the difference table. Therefore, the input pixel, as the boun− dary−falling pixel, must be abandoned for data embedding.…”

Section: Handled Exception Problemmentioning

confidence: 99%

See 1 more Smart Citation

Steganographic data hiding in image processing using predictive differencing

Weng

Tso²,

Wang

2012

Opto-Electronics Review

View full text Add to dashboard Cite

In this paper, we propose a stenography scheme based on predictive differencing to embed data in a grey-image. In order to promote the embedding capacity of pixel-value differencing (PVD), we use differencing between a predictive value and an input pixel as the predictive differencing to embed the message where a predictive value is calculated by using various predictors. If the predictive differencing is large, then it means that the input pixel is located in the edge area and, thus, has a larger embedding capacity than the pixel in a smooth area. The experimental result shows that our proposed scheme is capable of providing greater embedding capacity and high quality of stego-images then previous works. Furthermore, we have also applied various predictors to evaluate our proposed scheme.

show abstract

Section: Handled Exception Problemmentioning

confidence: 99%

“…The simplest scheme is the well−known method of least− −significant−bits (LSB) that hides the message into the LSB of cover pixels [9][10][11]. In general, the LSB approach is a direct substitution method where the least−bits of the pi− xels are replaced with message bits which results in a sche− me's high capacity.…”

Section: Introductionmentioning

confidence: 99%

Steganographic data hiding in image processing using predictive differencing

Weng

Tso²,

Wang

2012

Opto-Electronics Review

View full text Add to dashboard Cite

show abstract

“…In spatial domain, the secret message is embedded into cover by using direct replacing of least significant bits of cover with the bits of secret message. For example, the methods presented in [3] [4] are stated in this group. The main advantage of spatial steganography to transformation domain steganography is high capacity.…”

Section: Introductionmentioning

confidence: 99%

Secure Information Transmission using Steganography and Morphological Associative Memory

Nazari¹,

Eftekhari²,

Moin³

2013

IJCA

View full text Add to dashboard Cite

This paper presents a new steganography algorithm based on Morphology associative memory. Often, steganalysis methods are created to detect steganography algorithms using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). In this paper, cover images are mapped to morphological representation by using morphology transform containing morphological coefficients, and each bit of secret message is inserted in the least significant bit of morphological coefficients. To evaluate stego quality, we measure the quality of the cover image after embedding by comparing with other image transformed steganography algorithms such as discrete cosine and Wavelet transforms. The quality of stego has considerably improved in comparison with the state-of-art methods. In the other experimentation, we test the robustness of our proposed method by using Wavelet and Block-based steganalysis methods. The results show a high level of robustness of our algorithm respect to other steganography algorithms.

show abstract

“…In Wu's et al approach, GA was used for finding the desired matrices for transferring the blocks and value pixel of secret image to a new secret image based on comparing cover image and secret image. Another method is optimal pixel adjustment (OPA) that modifies simple LSB, the aim of which is to have a stego-image with a better quality [5].…”

Section: Introductionmentioning

confidence: 99%

Least Significant Bit Image Steganography using Particle Swarm Optimization and Optical Pixel Adjustment

Gerami¹,

Ebrahim²,

Bashardoost³

2012

IJCA

View full text Add to dashboard Cite

Data hiding is a method of concealing secret data into a cover media and preventing a spectator from being aware of the existence of the hidden message. According to the problems of steganography, the main effort is to provide a better imperceptibility of stego-image that can be done by decreasing distortion of image. One of the popular techniques in data hiding is steganography in which the simple method for image hiding is the Least Significant Bit (LSB) substitution method. Wu et al. proposed two image hiding techniques to improve the quality of the stego-image [1]. The first one is to find the best block matching matrix and the other one is to find the optimal substitution matrices. The proposed method utilizes particle Swarm optimization (PSO) for finding the best pixel locations, and then the secret image is transformed to a new secret image. Optimal Pixel Adjustment (OPA) method is applied to further increase the quality of image. Results are then compared with those obtained by Simple LSB, Wang's et al. method, Wu's et al. method. The experimental results confirmed that PSNR of the proposed method is higher than mentioned methods which mean the imperceptibility of the image improves. Also the results illustrate that the proposed approach is robust against chi-square attack.

show abstract

Image hiding by optimal LSB substitution and genetic algorithm

Cited by 618 publications

References 9 publications

Steganographic data hiding in image processing using predictive differencing

Steganographic data hiding in image processing using predictive differencing

Secure Information Transmission using Steganography and Morphological Associative Memory

Least Significant Bit Image Steganography using Particle Swarm Optimization and Optical Pixel Adjustment

Contact Info

Product

Resources

About