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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >Image steganalysis using improved particle swarm optimization based feature selection
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Image steganalysis using improved particle swarm optimization based feature selection

机译：使用改进的粒子群优化基于特征选择的图像隐藏

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摘要

Image steganalysis is the task of discovering the hidden message in a multimedia file in which the steganalysis technique highly depends on the feature elements of the image. Since there is a possibility for a feature vector to contain redundant elements, processing of redundant elements can be harmful in terms of long computation cost and large storage space. This paper proposes a new feature selection approach based on Adaptive inertia weight-based Particle Swarm Optimization (APSO) for the image steganalysis where the inertia weight of PSO is adaptively adjusted using the swarm diameter, average distance of particles around the center and average speed of particles towards the center. Also, the proposed APSO is used with the novel measure of Area Under the receiver operating characteristics Curve (AUC) as the fitness function to enhance the performance of identification of stego-images from the cover images in steganalysis problem. Due to appropriate convergence rate and the regulated search step of APSO, it is able to select the most significant and influential feature elements and so, the performance of steganalysis will be improved. Experimental results of the proposed method on Breaking Out Steganography System (BOSS) benchmark proves the superiority of the proposed method compared to the similar approaches in image steganalysis in terms of detection of stego-image, running time, and diversity measure.
机译：图像隐析是在多媒体文件中发现隐藏消息的任务，其中隐分技术高度取决于图像的特征元素。由于具有冗余元件的特征向量的可能性，因此在长的计算成本和大存储空间方面，冗余元件的处理可能是有害的。本文提出了一种基于自适应惯性权重的粒子群综合优化（APSO）的新特征选择方法，用于使用群直径，围绕中心围绕中心的平均距离和平均速度进行自适应调整PSO的惯性重量。朝向中心的颗粒。此外，所提出的APSO在接收器操作特性曲线（AUC）下的新颖性面积测量用作适合函数，以增强Sectany分析问题中的封面图像识别STEGO图像的性能。由于适当的收敛速度和APSO的受调节的搜索步骤，它能够选择最重要和有影响力的特征元素，因此，将提高隐草的性能。与释放隐写体系统（BOSS）基准的提出方法的实验结果证明了与在STEGO图像，运行时间和多样性度量的检测方面的图像隐分中的类似方法相比，所提出的方法的优越性。

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