Adaptive Spatial Steganography Based on the Correlation of Wavelet Coefficients for Digital Images in Spatial Domain

Steganslysis is an important research issue in information security. Aimed at the most commonly used cover media, digital images, the paper proposed a new method of universal steganalysis method based on multi-domain features. Features were extracted from spatial domain and DWT domain to overcome the drawbacks of steganslysis algorithm for specific steganography, such as high complexity and low correct detecting ratio. Experiment results show that the proposed algorithm can solve the low detecting problem and achieve a better reliability on low embedding rates.

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A FPGA based Steganographic System Implementing a Modern Steganalysis Resistant LSB Algorithm

Defence Science Journal ◽

10.14429/dsj.67.10177 ◽

2017 ◽

Vol 67 (5) ◽

pp. 551 ◽

Cited By ~ 1

Author(s):

Kunjan Pathak ◽

Manu Bansal

Keyword(s):

Computational Complexity ◽

Data Hiding ◽

Hardware Implementation ◽

Digital Images ◽

Spatial Domain ◽

Secret Message ◽

Original Algorithm ◽

Qualitative And Quantitative

<p>Steganography differs from other data hiding techniques because it encodes secret message inside cover object in such a way that transmission of secret message also remains a secret. Widespread usage of digital images, lower computational complexity and better performance makes spatial domain steganographic algorithms well suited for hardware implementation, which are not very frequent. This work tries to implement a modern steganalysis resistant LSB algorithm on FPGA based hardware. The presented work also optimises various operations and elements from original one third probability algorithm with respect to hardware implementation. The target FPGA for the implementation is Xilinx SP605 board (Spartan 6 series XC6SLX45T FPGA). Stego images obtained by the implementation have been thoroughly examined for various qualitative and quantitative aspects, which are found to be at par with original algorithm.</p>

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ON ESTIMATES OF COEFFICIENTS OF GENERALIZED ATOMIC WAVELETS EXPANSIONS AND THEIR APPLICATION TO DATA PROCESSING

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2020.1.05 ◽

2020 ◽

pp. 44-57 ◽

Cited By ~ 1

Author(s):

Victor Olexandrovych Makarichev ◽

Vladimir Vasilyevich Lukin ◽

Iryna Victorivna Brysina

Keyword(s):

Digital Image ◽

Digital Images ◽

Signal To Noise Ratio ◽

Expected Value ◽

Wavelet Expansion ◽

Correction Coefficient ◽

Quality Loss ◽

Wavelet Coefficients ◽

Absolute Deviation ◽

Real Value

Discrete atomic compression (DAC) of digital images is considered. It is a lossy compression algorithm. The aim of this paper is to obtain a mechanism for control of quality loss. Among a large number of different metrics, which are used to assess loss of quality, the maximum absolute deviation or the MAD-metric is chosen, since it is the most sensitive to any even the most minor changes of processed data. In DAC, the main loss of quality is got in the process of quantizing atomic wavelet coefficients that is the subject matter of this paper. The goal is to investigate the effect of the quantization procedure on atomic wavelet coefficients. We solve the following task: to obtain estimates of these coefficients. In the current research, we use the methods of atomic function theory and digital image processing. Using the properties of the generalized atomic wavelets, we get estimates of generalized atomic wavelet expansion coefficients. These inequalities provide dependence of quality loss measured by the MAD-metric on the parameters of quantization in the form of upper bounds. They are confirmed by the DAC-processing of the test images. Also, loss of quality measured by root mean square (RMS) and peak signal to noise ratio (PSNR) is computed. Analyzing the results of experiments, which are carried out using the computer program "Discrete Atomic Compression: Research Kit", we obtain the following results: 1) the deviation of the expected value of MAD from its real value in some cases is large; 2) accuracy of the estimates depends on parameters of quantization, as well as depth of atomic wavelet expansion and type of the digital image (full color or grayscale); 3) discrepancies can be reduced by applying a correction coefficient; 4) the ratio of the expected value of MAD to its real value behaves relatively constant and the ratio of the expected value of MAD to RMS and PSNR do not. Conclusions: discrete atomic compression of digital images in combination with the proposed method of quality loss control provide obtaining results of the desired quality and its further development, research and application are promising.

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Adaptive oriented filtration of digital images in the spatial domain

Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019 ◽

10.1117/12.2537165 ◽

2019 ◽

Author(s):

Serhiy V. Balovsyak ◽

Oleksandr V. Derevyanchuk ◽

Igor M. Fodchuk ◽

Olga P. Kroitor ◽

Khrystyna S. Odaiska ◽

...

Keyword(s):

Digital Images ◽

Spatial Domain

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Deblurring of digital images through a spatial domain approach

Journal of the Franklin Institute ◽

10.1016/0016-0032(89)90031-8 ◽

1989 ◽

Vol 326 (5) ◽

pp. 749-753

Author(s):

M.M. Mameri ◽

M.A. Sid-Ahmed

Keyword(s):

Digital Images ◽

Spatial Domain

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Auto-Layering Wavelet Transfer to Remove the Circumstance Effect and Noise

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.798-799.624 ◽

2013 ◽

Vol 798-799 ◽

pp. 624-629

Author(s):

Pang Da Dai ◽

Yu Jun Zhang ◽

Chang Hua Lu ◽

Yi Zhou ◽

Wei Zhang ◽

...

Keyword(s):

Relative Error ◽

Light Propagation ◽

Digital Images ◽

Energy Ratio ◽

Light Sources ◽

Wavelet Coefficients ◽

Multi Scale ◽

Transfer Method ◽

Night Condition

The accuracy of visibility measurement from night light sources image is usually affected by the circumstance light and noise. This paper presents an auto-layering wavelet transfer method to remove the circumstance effect and noise simultaneously. Firstly, the light propagation through the fog at night condition is formulized, where the model and features of night image with circumstance effect and noise is given. Secondly, we propose to use multi-scale features of wavelet transfer to decompose the image to remove the circumstance effect and noise, where an auto-layering method is used based on the energy ratio of wavelet coefficients. Experiments show that our method is able to remove the circumstance effect and noise simultaneously and to adjust the decomposed layering number automatically. Our method is not only suitable for many wavelet functions, but also preserves the light sources as well as their glows in the digital images. The relative error of using db4 is 3.16%, and the relative error of using sym2 is 2.02%.

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