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by
Jiaohua Qin |
Total Records (
8 ) for
Jiaohua Qin |
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Zhihua Xia
,
Xingming Sun
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Jiaohua Qin
and
Changming Niu
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Learning-based methodology has been demonstrated to be an effective approach to dispose the steganalysis difficulties due to the variety of image texture. A crucial process of the learning-based steganalysis is to construct a low-dimensional feature set. In this study, a feature selection method based on Hybrid Genetic Algorithm (HGA) is presented to select feature subsets which not only contain fewer features, but also provide better detection performance for steganalysis. First, the general framework about utilizing Genetic Algorithm (GA) to do feature selection for steganalysis is presented. Then, we analyze similarity among individuals (SI) in each generation and the Transformation of Generations (TG) to determine whether the GA has converged into a local area. Next, according to the SI and TG, the restarting operation is incorporated into the HGA to allow the algorithm to escape from the unsatisfactory local area. In the experiments, three feature subsets are formed from a universal feature set for three typical steganography methods, respectively. The experimental results show that the classifiers using the feature subsets gain better detection accuracy and higher speed than those using the universal set. |
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Jiaohua Qin
,
Xingming Sun
,
Xuyu Xiang
and
Zhihua Xia
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In this study, a new steganalytic method, which exploits the difference statistics of neighboring pixels, is proposed to detect the presence of spatial LSB matching steganography. In the proposed method, the differences between the neighboring pixels (DNPs), the differences between the local extrema (DLENs) and their neighbors in grayscale histogram are used as distinguishing features and the SVM is adopted to construct classifier. Experimental results show that the proposed method is efficient to detect the LSB matching steganography for the compressed and uncompressed images and outperforms other recently proposed algorithms. |
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Xuyu Xiang
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Dafan Zhang
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Jiaohua Qin
and
Yuanyuan Fu
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Multiple Sequence Alignment (MSA), known as NP-complete
problem, is among the most important and challenging tasks in computational
biology. For multiple sequence alignment, it is difficult to solve this
type of problems directly and always results in exponential complexity.
In order to effectively solve the MSA problem, in this study, we present
a novel algorithm of ant colony with genetic algorithm (ACG) based on
the planar graph representation for MSA. Firstly, the planar graph is
described a representation for multiple sequences that took every possible
aligning result into account by defining the representation of gap insertion,
the value of heuristic information in every optional path and scoring
rule for the processes of MSA. Secondly, we use an ant colony with genetic
algorithm to find the better path that denotes a better aligning result
for multidimensional graph. Experimental results show that ACG could bring
about a rise in the quality of MSA when compared with standard Clustal
algorithm. |
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Jiaohua Qin
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Xuyu Xiang
and
Meng Xian Wang
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LSB matching steganalysis techniques detect the existence of secret messages embedded by LSB matching steganorgaphy in digital media. This study presents a survey of LSB matching steganalysis methods for digital images. Firstly, study described the structure of LSB matching steganalysis, which includes three parts: LSB matching steganography, detectors for LSB matching and the evaluation methodology. Secondly, study classified the existing detection algorithms into two categories according to the fact that the main contribution of the algorithm is detector or estimator. For the detectors, study classified the existing various methods to two categories, described briefly their principles and introduced their detailed algorithms. For the estimators, study introduced the existing two estimating methods for LSB matching. Finally, study concluded and discussed some important problems in this field and indicated some interesting directions that may be worth researching in the future. |
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Rengarajan Amirtharajan
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Jiaohua Qin
and
John Bosco Balaguru Rayappan
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In the current corporate scenario, data or information security is the most significant asset because loss of information will lead to financial and market loss which in-turn will be the end of business. Though, the security guards like cryptography, watermarking, steganography have armed on the electromagnetic pathway against hackers, the concern on data protection is growing in parallel with the up-to-the-minute electronic technology. In this review, the role, strength and weakness of steganography especially different random image steganography techniques in protecting the data have been analyzed and in addition how random techniques can be made smarter and effective have also been explored. |
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Jiaohua Qin
,
Xiaoyu Guo
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XuyuXiang
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Lingyun Xiang
and
LiliPan
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This study was proposed by a steganalysis algorithm against Multiple Least Significant Bits (MLSB) steganography to estimate the secret message length in images. Firstly, a local masked estimation function by using weighted average of the masked pixels was used to estimate an approximately original image. Then, the weighted stego image was constructed by extending the definition of LSB steganalysis to MLSB one. Finally, the secret message length estimator was constructed through a least square equation. The detailed theoretical proofs of the detection and estimation methods were presented in this study. Experimental results revealed that this method can estimate the length of secret message with high accuracy and low time complexity. |
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Jiaohua Qin
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Xuyu Xiang
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Yu Deng
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Youyun Li
and
Lili Pan
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Highly undetectable steganography (HUGO) is one of the most
advanced steganographic systems. A new methodology of steganalysis is presented
against HUGO for digital images. The proposed method first obtains textural
features by applying local linear transformation of convolution filtering to
the image. Then, the co-occurrence matrices are constructed from horizontal
and vertical direction. Finally, the ensemble classifier is used to classify.
Experimental results show that the proposed steganalysis system is significantly
superior to the prior arts on the detection performance and computational time. |
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Weimin Zuo
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Xuyu Xiang
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Yuanyuan Fu
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Jiaohua Qin
and
Xiaoyu Guo
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The generating test study is a research in constrained multi-object
optimization. It is one of the key technologies in examination management systems.
It relates directly to the efficiency and quality of the generating test paper.
The test paper generation method suggested in this paper is based on hierarchical
adaptive genetic algorithm. It is able to solve the problem of premature convergence
or slow convergence in global optimization. On the one hand, the M subpopulations
operate on adaptive genetic algorithm and save the intermediate result; then
they operate with the top adaptive genetic algorithm until a satisfied paper
is found. On the other hand, the minimum weighted mean square error model is
used to establish the objective function and to inspect the error between the
expectations and the actual value on types, knowledge topics, difficulty and
the degree of differentiation of the test paper. It discusses also the error
of the answer time, total score and luminosity. It improved the speed of generating
test paper of the system. It avoided the problem of premature convergence which
often appears in standard genetic algorithm. The high quality of paper generation
and the good robustness generated in this algorithm can meet the practical needs
of users. |
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