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In this paper, we propose a steganalysis algorithm to detect spatial domain least significant bit (LSB) matching steganography, which is much harder than the detection of LSB replacement. We use features based on histogram of run length and histogram characteristic function to detect the LSB Matching. Experimental results on two datasets demonstrate that this method has superior results compared with...
In this paper, we propose a credit scoring algorithm to predict seceders of a large Japanese company. We use features based on attributes of customer and genetic algorithm based Bayesian network to detect the seceders. Experimental results on the datasets demonstrate that this method has superior results compared with other recently proposed algorithms, and shows that the proposed method is efficient...
An improved lossless data hiding scheme, which can restore the original image completely after data extraction, is presented in this paper. In the proposed method, a cover image is reordered into a sequence using space filling curves. For each pixel (except the first pixel) in the sequence, a pixel difference is generated between the pixel and its neighbor pixel. Due to the inherent structure of images...
We proposes a method to estimate secret message length of secret message embedded using quantization index modulation (QIM) based data hiding in digital images. The Secret Message Length estimation equation is constructed used for embedding ratio estimation. Our experimental results have demonstrated that the proposed steganalyzers can is a reliably way to estimate the secret message length of QIM...
In this paper, we propose a feature generation and classification approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. The GA is utilized to select a subset of candidate features, a subset of candidate transformations to generate new features. The logistic regression model and Bayesian network model are then used as the classifier. Experimental results show...
This paper presents a novel LSB steganographic scheme which attempts to defeat statistical steganalysis. The embedding distortion introduced to the cover image is small for perceptual and statistical detection. The modification of image pixel values for embedding is just like conventional image processing which is capable of resisting all kinds of LSB steganalysis attacks such as the Chi-Squre test,...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on Genetic Algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
In this paper, we utilized machine vision and image processing to develop an image detection flow for the dimension and appearance of multilayer ceramic capacitors (MLCC), and also used proposed automatic optical inspection (AOI) system in the MLCC production line operation. We compared the advantages and disadvantages of Hough Transform and Histogram analysis. The proposed tiny passive components...
A lossless data hiding scheme, which can restore the original image completely after data extraction, is presented in this paper. In the proposed method, a cover image is reordered into a sequence using space filling curves. For each pixel (except the first pixel) in the sequence, a pixel difference is generated between the pixel and its neighbor pixel. Due to the inherent structure of images causes...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
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