Convolution Neural Networks for Blind Image Steganalysis: A Comprehensive Study

Abstract

Recently, Convolution Neural Network is widely applied in Image Classification, Object Detection, Scene labeling, Speech, Natural Language Processing and other fields. In this comprehensive study a variety of scenarios and efforts are surveyed since 2014 at yet, in order to provide a guide to further improve future researchers what CNN-based blind image steganalysis are presented its architecture, performance and limitations. Long-standing and important problem in image steganalysis difficulties mainly lie in how to give high accuracy and low payload in stego or cover images for improving performance of the network.

Palm vein recognition based on convolution neural network

“…Multi-Layer Perceptron (MLP) It is a forward feeding structure for artificial neural networks containing more hidden layers, and each layer consists of a simple, mathematically connected combination of the contract known as neuro cells as shown in Fig. 2 [12][13] [14].…”

“…The main purpose of using the activation function is to give the irritation to extract nervous cells, for learning and carrying out complex tasks. Back Propagation makes it possible the most active function used is the corrected written unit (ReLU), and equation ( 3) illustrates the representation of the RLU [14].…”

“…The most important characteristic of the NN is local communication and the use of common weights, so that they can identify features local of the portray [14].…”