No abstract

Steganography surpasses other mechanisms of securing data from potential threats. The modern digital arena calls for robust information hiding techniques and, thus, it has always been a flash point for researchers and academicians. Nowadays, transmission is susceptible to numerous hacks while sharing secret information through typical correspondence channel. Accordingly, everybody needs the classification, respectability, and realness of his or her privileged information. Particularly, different techniques are used to take on these security issues like advanced declaration, computerized mark, and cryptography. Nevertheless, these strategies alone cannot be negotiated. Steganography is a revolution where current information compression, data hypothesis, spread range, and cryptography advancements are integrated to meet the requirements for protection of data over the Internet. This study investigates and critically analyses various existing cover steganography techniques and identifies the valuable region that everyone can be benefited. Moreover, we present a comprehensive overview of the fundamental concepts with in the domain of the steganographic methods and steganalysis. These systems have been depicted in numerous areas of the steganography such as spatial space, transform domain, and adaptive space. Moreover, each space has its particular traits. A few regularly involved techniques for improving the steganographic security and upgrading steganalysis capacity are elaborated, summed up; and conceivable examination patterns are talked about. We also systematically separate different methodologies in our review and show their pros and cons, qualities, challenges and significance.INDEX TERMS Steganography, data concealing, cover objects, image quality assessment metrics.

The Internet and Big Data expansion have motivated the requirement for more generous stockpiling to hold and share information. Against the current era of information, guaranteeing protection and security to individuals sending data to each other is of utmost importance. The only file type that is instantly and widely used is the image. Therefore, to secure transmission, it is necessary to develop a mechanism to safeguard user data transmission. Considering this thought, it is necessary to analyze the best file type of image for essential criteria of image steganography, such as Payload, Robustness, Imperceptibility, etc., to challenge the weakness of the current algorithms. The widely used image formats are PNG, TIFF, JPEG, BMP, and GIF, which is the cause of existing methods. However, in this case, the critical softness is the credibility of the steganography, which plays a vital role in these format images to ensure the end users communicate. In this paper, a single algorithm provides several advantages for various types of images used as cover objects. However, after the critical and comparative analysis of different perspectives and some assessment metrics, the experimental results prove the importance, significance, and promising limits for these image formats by accomplishing a 4.4450% normal higher score for PSNR correlation than the next best existing methodology. Besides, in PSNR with a variable measure of code implanted in similar pictures of similar aspects, the proposed approach accomplished a 6.33% better score. Encrypting similar code sizes in pictures of various dimensions brought about a 4.23% better score. Embedding the same message size into the same dimension of different images resulted in a 3.222% better score.

It is essential in the field of cover steganography to track down a mechanism for concealing data by utilizing different blends of compression strategies. Amplifying the payload limit, robust, and working on the visual quality is the essential factors of this research to make a reliable mechanism. We can’t compromise on image quality up to a confident flat because it halts the concepts of cover steganography while the maximum embedding limit is also the main factor that makes the technique more efficient. So, Image steganography is the state of art method that hides a data inside any cover mediums such as images, videos, texts, audios etc. Steganography is the specialty of implanting a mystery message so that nobody can think of it or no unaided eye can identify it. There is no information on the current information inside the cover object in the wake of encrypting. In the recent couple of years, due to the achievement of accelerated popularity of the internet, various organizations such as government offices, military, private companies etc. use different transferring methods for exchanging their information. The internet has various benefits and a few demerits. The primary bad mark is protection and security and information transmission over an unreliable network. Different cover steganography research strategies have been recommended as of late yet, and each adores its benefits and impediments but, there is the need to foster some better cover steganography implements to accomplish dependability between the essential model of cover steganography. To handle these issues, this paper proposed a future method in view of Huffman code, Least Significant Bits (LSB) based cover steganography utilizing Multi-Level Encryption (MLE) and colorless part (HC-LSBIS-MLE-AC) of the picture. It also used different substitution and flicking concept, MLE, Magic matrix, and achromatic concepts for proving the proficiency, and significance of the method. The suggested method used an encrypted algorithm to upheld the appearances of the image. The algorithm also statistically investigated based on some Statistical Assessment Metrics (SAM) such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC), Structural Similarity Index Metric (SSIM) etc. and different perspectives. The observational outcomes show the likelihood of the proposed algorithm and the capacity to give unwavering quality between security, payload, perception, computation, and temper protection.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.