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It is increasingly difficult to identify data hiding techniques used in the stego-images in this era of great advancement in technology. The purpose of this paper is to identify the types of data hiding techniques of stego-images. This paper proposed the use deep learning and convolutional neural networks (CNN) to train and generate a binary classification model for prediction. This paper proposed three types of detection algorithms for stego-images. The algorithms are based on the LeNet technique of convolutional neural network (CNN) in deep learning. The first algorithm detects the DCT stego-images. The second detects the histogram stego-images. The third and final algorithm detects the LSB stego-images. Experimental results from LeNet deep learning show significantly high detection rate. The DCT stego-images showed a significant high detection accuracy of 99%, while the LSB and histograms stego-images showed 70% accuracy rate. Introduction To date, many data hidden techniques had been proposed to protect information. In order to pass confidential information, many departments hide important the information into images to avoid being discovered. Some use includes the military for passing secret action plan. Currently, data hiding is one of the most effective ways to deliver hidden messages. As computers evolve rapidly, data hiding techniques are becoming more diverse too. Data hiding can be divided into reversible data hiding [1] and irreversible data hiding. Reversible data hiding hides confidential information into images, and after extracting confidential information hidden in the images, the original images would be restored without any damages. The irreversible data hiding is where the embedding and extraction processes will damage the original image. An image with hidden data is called a stego image. With the development of image data hiding technology, the features of the newly proposed data hiding method are gradually becoming more complicated. At present, models such as SRM (Spatial Rich Model) [2] and PSRM (Projection Spatial Rich Model) [3] have achieved good detection results. However, in the traditional manual feature detection environment, the new type of data hiding can easily avoid the traditional detection method. As a result, it is impossible to adapt to the new data hiding techniques. However, there are many hidden images today with no good detection mechanism. Therefore, this paper proposed deep learning to identify multiple stego images and to use convolutional neural networks for image training where important features of an image are extracted. The method identifies three different stego image, namely, DCT stego images [4-5], histogram stego images [6] and LSB stego images [7]. In training images by the convolutional neural network, three of binary classification models are generated for the predictive analysis. Various classification results are generated through the model to determine which kind of data hiding is detected in the stego image by deep learning.

A LeNet Based Convolution Neural Network for Image Steganalysis on Multiclass Classification