The digital information revolution has brought about changes in our society and our lives. The many advantages of digital information have also generated new challenges and new opportunities for innovation. The strength of the information hiding science is due to the nonexistence of standard algorithms to be used in hiding secret message. Also, there is randomness in hiding method such as combining several media (covers) with different methods to pass secret message. Information hiding represents a class of process used to embed data into various forms of media such as image, audio, or text. The proposed text in image cryptography and steganography system (TICSS) is an approach used to embed text into gray image (BMP). TICSS is easily applied by any end user.
The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predened nite number of gesture classes.The main objective of this eort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Articial neural network is built for the purpose of classication by using the back-propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six specic static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classication. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage.In the rst method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however,
Six algorithms that design secrecy keys are used for digital image encryption. The privacy keys generated by stream cipher generators were tested for their randomness applying five tests of randomness. The images generated by applying each algorithm were tested for their regularity and residual intelligibility. The histograms for images ciphered by stream cipher schemes all have approximately flat histogram less information as compared to one-dimensional ciphering algorithms and threshold generator-based image ciphering scheme. Furthermore, the stream cipher schemes were effectively applied to the colored images of 256-color levels of true-color images (24 bit). In this paper, we try to decrypt automatically using artificial neural network by decryption through multilayer perceptron and radial basis function; networks were tested using the interface by calculating the error rates of decrypted images.
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