Modified LSB Watermarking for Image Authentication

This paper mainly aims at developing an authentication scheme for digital images. The LSB scheme is chosen base for our proposed work. Through literature survey it is found that conventional LSB scheme provides low embedding rat low distortion and is irreversible. Because of its irreversibility, the conventional LSB scheme cannot be used for critic applications where reversibility is mandatory. Through this literature survey, we learnt that this conventional LSB scheme us only one bit in every pixel for embedding. Our proposed scheme presents a modified LSB embedding strategy that satisfies th reversibility and improves the embedding rate by using two bits in every pixel for embedding

Efficient Classification of Satellite Image with Hybrid Approach Using CNN-CA

Today, satellite imagery is being utilized to help repair and restore societal issues caused by habitats for a variety of scientific studies. Water resource search, environmental protection simulations, meteorological analysis, and soil class analysis may all benefit from the satellite images. The categorization algorithms were used generally and the most appropriate strategies are also be used for analyzing the Satellite image. There are several normal classification mechanisms, such as optimum likelihood, parallel piping or minimum distance classification that have presented in some other existing technologies. But the traditional classification algorithm has some disadvantages. Convolutional neural network (CNN) classification based on CA was implemented in this article. Using the gray level Satellite image as the target and CNN image classification by the CA’s selfiteration mechanism and eventually explores the efficacy and viability of the proposed method in long-term satellite remote sensing image water body classification. Our findings indicate that the proposed method not only has rapid convergence speed, reliability but can also efficiently classify satellite remote sensing images with long-term sequence and reasonable applicability. The proposed technique acquires an accuracy of 91% which is maximum than conventional methods