Blind JPEG Steganalysis Using Features Derived from Multi-Domain

2011 ◽  
Vol 50-51 ◽  
pp. 693-699
Author(s):  
Wen Qiong Yu ◽  
Zhuo Li ◽  
Ling Di Ping
Keyword(s):  
Intermediate Frequency ◽  
Spatial Domain ◽  
Experimental Results ◽  
Spatial Features ◽  
Dct Domain ◽  
Jpeg Steganography

In this paper, a new blind steganalytic scheme is proposed to effectively detect JPEG steganography. In DCT domain, the co-occurrence matrices are used to capture the intra-block and inter-block correlations among the quantized coefficients. In spatial domain, we estimate the invariant components of the decompressed raw pixels between the cover and stego images by wiping selected intermediate frequency AC coefficients, and collect eighteen statistics in total. Combining the DCT and spatial features leads to be more effective for classification. The experimental results have demonstrated that the proposed method is stable, and outperforms the recently reported steganalysis in attacking the advanced JPEG steganography.

scholarly journals Random Fiber Grating Characterization Based on OFDR and Transfer Matrix Method

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6071
Author(s):  
Zichao Zhou ◽  
Chen Chen ◽  
Ping Lu ◽  
Stephen Mihailov ◽  
Liang Chen ◽  
...  
Keyword(s):  
Fiber Lasers ◽  
Spectral Response ◽  
Quantitative Relationship ◽  
Spatial Domain ◽  
Experimental Results ◽  
Period Variation ◽  
Peak Wavelength ◽  
Variation Range ◽  
Wavelength Variation ◽  
Simulation Results

Random fiber gratings (RFGs) have shown great potential applications in fiber sensing and random fiber lasers. However, a quantitative relationship between the degree of randomness of the RFG and its spectral response has never been analyzed. In this paper, two RFGs with different degrees of randomness are first characterized experimentally by optical frequency domain reflectometry (OFDR). Experimental results show that the high degree of randomness leads to low backscattering strength of the grating and strong strength fluctuations in the spatial domain. The local spectral response of the grating exhibits multiple peaks and a large peak wavelength variation range when its degree of randomness is high. The linewidth of its fine spectrum structures shows scaling behavior with the grating length. In order to find a quantitative relationship between the degree of randomness and spectrum property of RFG, entropy was introduced to describe the degree of randomness induced by period variation of the sub-grating. Simulation results showed that the average reflectivity of the RFG in dB scale decreased linearly with increased sub-grating entropy, when the measured wavelength range was smaller than the peak wavelength variation range of the sub-grating. The peak reflectivity of the RFG was determined by κ2LΔP (where κ is the coupling coefficient, L is the grating length, ΔP is period variation range of the sub-grating) rather than κL when ΔP is larger than 8 nm in the spatial domain. The experimental results agree well with the simulation results, which helps to optimize the RFG manufacturing processes for future applications in random fiber lasers and sensors.

A PUBLIC MESH WATERMARKING ALGORITHM BASED ON ADDITION PROPERTY OF FOURIER TRANSFORM

2006 ◽  
Vol 06 (01) ◽  
pp. 35-43 ◽  
Author(s):  
LI LI ◽  
ZHIGENG PAN ◽  
DAVID ZHANG
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Spatial Domain ◽  
Experimental Results ◽  
Original Image ◽  
The Fourier Transform

This paper presents a public mesh watermarking algorithm whereby the resultant watermarked image minus the original image is the watermark information. According to the addition property of the Fourier transform, a change of spatial domain will cause a change in the frequency domain. The watermark information is then scaled down and embedded in one part of the x-coordinate of the original mesh. Finally, the x-coordinate of the test mesh is amplified before extraction. Experimental results prove that our algorithm is resistant to a variety of attacks without the need for any preprocessing.

Fully developed periodic turbulent pipe flow. Part 1. Main experimental results and comparison with predictions

1983 ◽  
Vol 137 ◽  
pp. 31-58 ◽  
Author(s):  
S. W. Tu ◽  
B. R. Ramaprian
Keyword(s):  
Experimental Data ◽  
Pipe Flow ◽  
Shear Flows ◽  
Volumetric Flow Rate ◽  
Intermediate Frequency ◽  
Turbulent Pipe Flow ◽  
Experimental Results ◽  
Turbulent Shear ◽  
Relevant Parameter ◽  
Flow Modulation

The present paper is the first part of a two-part report on a detailed investigation of periodic turbulent pipe flow. In this investigation, experimental data on instantaneous velocity and wall shear stress were obtained at a mean Reynolds number of 50000 in a fully developed turbulent pipe flow in which the volumetric flow rate was varied sinusoidally with time around the mean. Two oscillation frequencies at significant levels of flow modulation were studied in detail. The higher of these frequencies was of the order of the turbulent bursting frequency in the flow, and the other can be regarded as an intermediate frequency at which the flow still departed significantly from quasi-steady behaviour. While a few similar experiments have been reported in the recent literature, the present study stands out from the others in respect of the flow regimes investigated, the magnitude of flow modulation, the detailed nature of the measurements and most importantly the identification of a relevant parameter to characterize unsteady shear flows. The present paper contains the main experimental results and comparisons of these results with the results of a numerical calculation procedure which employs a well-known quasi-steady turbulence closure model. The experimental data are used to study the manner in which the time-mean, the ensemble-averaged and the random flow properties are influenced by flow oscillation at moderate to high frequencies. In addition, the data are also used to bring out the capability and limitations of quasi-steady turbulence modelling in the prediction of unsteady shear flows. A further and more detailed analysis of the experimental data, results of some additional experiments and a discussion on the characterization of turbulent shear flows are provided in Part 2 (Ramaprian & Tu 1983).

scholarly journals Image Denoising Based on Dilated Singularity Prior

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Shaoxiang Hu ◽  
Zhiwu Liao ◽  
Wufan Chen
Keyword(s):  
Em Algorithm ◽  
Image Denoising ◽  
Mathematical Morphology ◽  
Visual Quality ◽  
Spatial Domain ◽  
Experimental Results ◽  
Canny Operator ◽  
Nonlocal Means ◽  
Spatial Image ◽  
Noisy Images

In order to preserve singularities in denoising, we propose a new scheme by adding dilated singularity prior to noisy images. The singularities are detected by canny operator firstly and then dilated using mathematical morphology for finding pixels “near” singularities instead of “on” singularities. The denoising results for pixels near singularities are obtained by nonlocal means in spatial domain to preserve singularities while the denoising results for pixels in smooth regions are obtained by EM algorithm constrained by a mask formed by downsampled spatial image with dilated singularity prior to suiting the sizes of the subbands of wavelets. The final denoised results are got by combining the above two results. Experimental results show that the scheme can preserve singularity well with relatively high PSNR and good visual quality.

scholarly journals COMPUTER AIDED ANALYSIS OF LUNG CT BASED ON TRANSFORM DOMAIN FEATURES

2014 ◽  
pp. 244-249
Author(s):  
V. MINNAL
Keyword(s):  
Wavelet Transforms ◽  
Spatial Domain ◽  
Classification Performance ◽  
Transform Domain ◽  
Pixel Intensity ◽  
Second Stage ◽  
Contour Segmentation ◽  
Spatial Features ◽  
Computer Aided Analysis ◽  
Combined Feature

As Many CADx systems have been developed to detect lung cancer based on spatial domain features that process only the pixel intensity values, the proposed scheme applies frequency transform to the lung images to extract frequency domain features and they are combined with spatial features so that the features that are not revealed in spatial domain will be extracted and the classification performance can be tuned up. The proposed CADx comprises of four stages. In the first stage, lung region is segmented using Convexity based active contour segmentation. At second stage ROIs are extracted using spatially constrained KFCM clustering. Followed by standard wavelet transforms is applied on ROI so that transform domain features are extracted with shape and haralick histogram features. Finally neural network is trained by combined feature set to identify the cancerous nodules. Our proposed scheme has shown sensitivity of 95% and specificity of 96%.

scholarly journals 3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images

2019 ◽  
Vol 11 (7) ◽  
pp. 883 ◽  
Author(s):  
Majid Seydgar ◽  
Amin Alizadeh Naeini ◽  
Mengmeng Zhang ◽  
Wei Li ◽  
Mehran Satari
Keyword(s):  
Spatial Information ◽  
Short Term Memory ◽  
Structural Data ◽  
Experimental Results ◽  
Spatial Feature ◽  
Computational Burden ◽  
Spatial Classification ◽  
Spatial Features ◽  
Proposed Model

Nowadays, 3-D convolutional neural networks (3-D CNN) have attracted lots of attention in the spectral-spatial classification of hyperspectral imageries (HSI). In this model, the feed-forward processing structure reduces the computational burden of 3-D structural processing. However, this model as a vector-based methodology cannot analyze the full content of the HSI information, and as a result, its features are not quite discriminative. On the other hand, convolutional long short-term memory (CLSTM) can recurrently analyze the 3-D structural data to extract more discriminative and abstract features. However, the computational burden of this model as a sequence-based methodology is extremely high. In the meanwhile, the robust spectral-spatial feature extraction with a reasonable computational burden is of great interest in HSI classification. For this purpose, a two-stage method based on the integration of CNN and CLSTM is proposed. In the first stage, 3-D CNN is applied to extract low-dimensional shallow spectral-spatial features from HSI, where information on the spatial features are less than that of the spectral information; consequently, in the second stage, the CLSTM, for the first time, is applied to recurrently analyze the spatial information while considering the spectral one. The experimental results obtained from three widely used HSI datasets indicate that the application of the recurrent analysis for spatial feature extractions makes the proposed model robust against different spatial sizes of the extracted patches. Moreover, applying the 3-D CNN prior to the CLSTM efficiently reduces the model’s computational burden. The experimental results also indicated that the proposed model led to a 1% to 2% improvement compared to its counterpart models.

An Algorithm to Control Watermarking Capacity Using PSNR

2014 ◽  
Vol 989-994 ◽  
pp. 2368-2372
Author(s):  
Yong Xing ◽  
Dao Shun Wang
Keyword(s):  
Image Watermarking ◽  
Region Of Interest ◽  
Good Control ◽  
Spatial Domain ◽  
Experimental Results ◽  
Stego Image ◽  
Trade Off

Some existing methods for estimating image watermarking capacity may degenerate the quality of stego-image when the estimated capacity is achieved. In this paper, based on the Region of Interest (ROI) model, we discuss on the capacity problem and the quality of stego-images using PSNR. Considering the trade-off between capacity and quality, our method estimates the allowable distortion for each pixel in spatial domain in terms of the specific PSNR of stego-image. Experimental results show our method ensures a good control over the capacity without introducing artifacts.

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