The spectacular progress of technology related to the information and communication arena throughout the past epoch made the internet a powerful media for faster communication of data. Though this technology is being admired at one side, there equally exists a challenge for safeguarding the data and privacy of information of a personal without any leak in the data and corresponding mistreatment. Hence, the proposed work primarily aims to investigate the internet communication as well as deter any unwanted happenings, which could occur because of the covert communication. The probable presence of hidden messages is inspected in the digital mass media using the technique of steganalysis. The distinctive features are to be identified, chosen and extracted for universal (blind) steganalysis and are decided by the format of image and its transformation. In this paper, the analysis is carried out in JPEG format images and 10% embedding with 10 fold cross validation. The technique of calibration is used to obtain an estimate of the cover image. Four embedded techniques that have been applied for stegananlysis are Least Significant Bit Matching, LSB Replacement, Pixel Value Differencing (PVD) and F5 respectively. Four different sampling like linear, shuffle, stratified and automatic are considered in this paper. The classifiers used for a comparative study are Support Vector Machine (SVM) and SVM- Particle Swarm Optimization (SVM-PSO). Several kernels namely linear, epanechnikov, multi-quadratic, radial, ANOVA and polynomial are used in classification. The classifier is trained to examine every single coefficient as a separate unit for analysis and the outcome of this analysis helps in finding the decision of steganalysis.

在过去的时代中，与信息和通信领域有关的技术取得了惊人的进步，使互联网成为了一种强大的媒体，可以更快地进行数据通信。尽管一方面赞扬了该技术，但同样存在着保护个人数据和信息隐私而又不泄漏任何数据和相应的虐待的挑战。因此，拟议的工作主要旨在调查互联网通信并阻止由于隐蔽通信而可能发生的任何不良事件。使用隐写分析技术，可以在数字大众媒体中检查隐藏消息的可能存在。识别，选择和提取特征以进行通用（盲）隐写分析，并取决于图像的格式及其转换。在本文中，分析是在JPEG格式的图像中进行的，并且10％嵌入和10倍交叉验证。校准技术用于获取封面图像的估算值。应用于隐身分析的四种嵌入式技术分别是最低有效位匹配，LSB替换，像素值差（PVD）和F5。本文考虑了四种不同的采样，如线性，随机，分层和自动。用于比较研究的分类器是支持向量机（SVM）和SVM-粒子群优化（SVM-PSO）。分类中使用了几个核，即线性，epanechnikov，多二次，径向，ANOVA和多项式。