Steganography is one of the vital topics in the field of information security. In this paper we propose, a new Steganography algorithm that generates and brings out steganographic secret information hidden in images. In the proposed algorithm, the cover image is partitioned into four non overlapping blocks and the confidential data bits are also divided. The data bits are embedded in scattered way into the four blocks. In each pixel either single colour channel or two colour channels are used to embed the confidential data bits depending upon the size of the data bits. The proposed method generates low MSE value and high PSNR value. The experimental outcome shows, the algorithm can overcome drawbacks of many existing algorithms.
The surface grading of ceramic tiles is essential for ceramic tile industries due to the huge development of infrastructure and essential usage of ceramic tiles. In some industries, surface grading is performed manually. It is a difficult task due to a large number of variations in the surface properties. In this study, a technique for surface grading of ceramic using deep learning is presented. The system uses the VxC Tiles of Surface Grading (TSG) database for performance evaluation. The deep learning based Convolution Neural Network (CNN) is used for the surface grading approach that classifies the tiles into Grade-1 (G1), Grade-2 (G2) and Grade-3 (G3). The system uses seven layers in CNN, which includes convolution, pooling and fully connected layers. Initially, the input tile image is converted Red, Green and Blue (RGB) color channels, and then CNN approach is applied for the classification of tile images. Experimental results show the better classification accuracy of 96.17% for surface grading of ceramic tiles using a deep learning approach.
Nowadays, the user of the internet is growing very fast, in which sends and receiving a messages become very easy using social media applications, meanwhile using these applications, the security is a very big issue. Today providing security for the essential data becomes too hard, intruders become smarter. They are using advanced techniques and models to access our data. For a long period cryptography algorithms were operated to protect the important data, but nowadays these algorithms are easily broken by the intruders. The steganography algorithms are considered as the next generation of cryptography; every user is able to create own algorithms to send and receive the important data. In this method, the secret data will embed into image pixel; many more algorithms are designed by the researcher using this idea. All of these algorithms embed the data into an image and transfer the stego image from one end to another end with stegokey at the receiver end with the help of a stego-key, they do the reverse engineering process in the stego image to get the original data which embedded by the sender side. In most of the algorithms, transfer the stego image and key is a very big concern. Since during the transmission time of stego image and key, anyone can make changes into that; like resize the image or cropping the same. If the receiver gets the damaged version of stego image, they can’t get the original message back. In that circumstances transmission of stego-image with stego-key, it needs more space and time to reach the destination as well as need to pay attention to the security. To overcome these problems, the proposed method does not transfer the stego image, due to which it is not required to compare the image before and after data insertion and no need to calculate the peak signal-noise ratio (PSNR). It shares stego key with the proper security key to recognize if any intruder made an attack on that. This method provides good security for the data
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.