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Open access articles are freely available for download Abstract In order to improve the data-embedding capacity of modification-free steganography algorithm, scholars have done a lot of research work to meet practical demands. Steganogoraphy researching the user's behavioral habits of several social platforms, a semi-structured modification-free steganography algorithm is introduced in the Steganoography Research Paper. By constructing the mapping relationship between small icons and binary numbers, the idea of image stitching is utilized, and small icons are stitched together according to Steganoography Research Paper behavioral habits of people's social platforms to implement the graphical representation of secret messages. The convolutional neural network CNN has been used to train the small icon recognition and classification data set in the algorithm.

In order to improve the robustness of the algorithm, the icons processed by various attack methods are introduced as interference samples in the training set. The experimental results show that the algorithm has good anti-attack ability, and the hiding capacity can be improved, which can be used in the covert communication. Article Preview Top 1. Introduction Steganography is a technique of hiding the secret information in the carrier and extracting the secret information from the stego carrier, so as to achieve the purpose of the covert communication and copyright protection. Digital image is a common carrier which is often used in hiding information due to its large redundancy and wide application. Steganoography Research Paper the traditional information hiding method, digital images are embedded with secret messages, which lead to the modification of the carrier itself.

1. Introduction

These modifications can cause some characteristics of the image to change. The third party determines whether the picture is embedded in the secret message by extracting these features. Though traditional information hiding has better robustness and larger capacity, it is difficult to resist steganography analysis and detection.

In order to improve the security of covert communication, Modification-free Steganography algorithm has attracted extensive attention Cao et al. It uses a certain feature of the image to establish a one-to-one mapping relationship with the binary sequence. Texture synthesis information hiding implements the information hiding in the process of texture synthesis, and the resulting Steganoography Research Paper texture image is related to secret information. But the latest research shows that this method still has security holes Zhou et al. Reference Xu et al. First, the secret information is directly written on the white Steganoography Research Paper, and then the background pattern and color coordinated with the color shape of the secret information are added to the blank portion, and finally the texture map is generated by using different deformation functions.

But the information which is hidden in the above method is Steganoography Research Paper text or a pattern with meaning, so it is not suitable for the hiding of binary data. But the above method has the problem of low practicality. Other scholars have applied deep learning methods into information hiding. Volkhonskiy et al. The anti-learning is to make the cover image and the stego image closer to improve the steganography security. But the generated cover image is embedded using the traditional steganography method in the end Volkhonskiy et al. The HayesGAN model proposed by Hayes uses the confrontation learning to directly generate the dense image. To address the above problems, this paper proposes a semi structured modification -free steganog- raphy algorithm based on the behavioral habits of social platform Zhang et al.

The specific implementation method is Steganoography Research Paper splice small icons into pictures under the guidance of the text rules to achieve modification-free. Among them, the library is established on the basis of training, classification and recognition of small icons with the method of deep learning.

Journal of Electrical and Computer Engineering

CNN Convolutional neural network Liu et al. The identification and classification of those small icons in the library are implemented in accordance with the high dimensional characteristic of images. Provided that the image may be attacked by the third Steganoography Research Paper http://pinsoftek.com/wp-content/custom/summer-plan-essay/judith-butler-gender-identity-analysis.php the process of transmission, and the image data-set should contain all kinds of interfered samples for training. These samples are the images which are specially processed. The training set containing the interference samples can ensure that the trained CNN network can correctly classify the icon after the attack to the stego images, which strengthens the robustness of the algorithm.

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