AbstractRecently the concept of 'Image Steganography' is became an important issue in the computer security world. Image steganography simply means hide some secret data into an object. The object can be a text, an image or a sound, but the most popular cover object used for hidden secret message is images.A developed detection system is introduced in this paper. The first part of the work includes creating varieties of stegoimages. These stego-images are having different image file formats. Also, these stego-images have been done using three steganography tools. They are: OpenStego, S-Tools and F5 algorithm. The created stego-images are used to train the detection system in the next stage.However, the second part of the work includes detecting the hidden data. In order to do so the co-occurrence matrix is created for all images. Number of image features is extracted from the matrix. Later, a discriminator will be used to validate the features selected. These features are needed it to differentiate between the clean images and the stego-images.Index TermsSteganography, image steganography, image steganalysis, stego-images.

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Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.