A lot of techniques are used to protect and hide information from any unauthorized users such as Steganography and Cryptography. Steganography hides a message inside another message without any suspicion, and Cryptography scrambles a message to conceal its contents. This paper uses a new text steganography that is applicable to work with different languages, the approach, based on the Pseudorandom Number Generation (PRNG), embeds the secret message into a generated Random Cover-text. The output (Stego-Text) is compressed to reduce the size. At the receiver side the reverse of these operations must be carried out to get back the original message. Two secret keys (Hiding Key & Extraction Key) for authentication are used at both ends in order to achieve a high level of security. The model has been applied to different message languages and both encrypted and unencrypted messages. The experimental results show the model"s capacity and the similarity test values..
In this study, we develop a reliable and highperformance multi-layer feed-forward artificial neural networks (MFANNs) model for predicting gender classification. The study used features for a set of 450 images randomly chosen from the FERET dataset. We extract the only high-merit candidate parameters form the FERET dataset. A discrete cosine transformation (DCT) is employed to facilitate an image description and conversion. To reach the final gender estimation model, authors examined three artificial neural classifiers and each extremely performs deep computation processes. In addition to the MFANNs, artificial neural networks (ANNs) classifiers include support vector regression with radial-basis function (SVR-RBF) and k-Nearest Neighbor (k-NN). A 10-folds cross-validation technique (CV) is used to prove the integrity of the dataset inputs and enhance the calculation process of the model. In this model, the performance criteria for accuracy rate and mean squared error (MSE) are carried out. Results of the MFANNs models are compared with the ones that obtained by SVR-RBF and k-NN. It is shown that the MFANNs model performs better (i.e. lowest MSE = 0.0789, and highest accuracy rate = 96.9%) than SVR-based and k-NN models. Linked the study findings with the results obtained in the literature review, we conclude that our method achieves a recommended calculation for gender prediction.
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