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Abstract

Abstract-Steganography is the method of information hiding. Free selection of cover image is a particular preponderance of steganography to other information hiding techniques. The performance of steganographic system can be improved by selecting the reasonable cover image. This article presents two level unsupervised image classification algorithm based on statistical characteristics of the image which helps Sender to make reasonable selection of cover image to enhance performance of steganographic method based on his specific purpose. Experiments demonstrate the effect of classification in satisfying steganography requirements.

Multilayer Perceptrons, Recurrent neural networks, Convolutional networks, and others types of neural networks are widespread nowadays. Neural Networks have hyperparameters like number of hidden layers, number of units for each hidden layer, learning rate, and activation function. Bayesian Optimization is one of the methods used for tuning hyperparameters. Usually this technique treats values of neurons in network as stochastic Gaussian processes. This article reports experimental results on multivariate normality test and proves that the neuron vectors are considerably far from Gaussian distribution.

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