Research On Color Image Steganography Based On Compression Encryption And Deep Learning

In the information age,images have gradually become the main medium of information communication due to their intuitive and vivid advantages.However,there are security risks in image transmission,and attacks on images containing sensitive information will lead to the disclosure of private data.Therefore,it is necessary to study the secure transmission of images.Image encryption can protect content security,but it is easy to attract attention,leading to information being intercepted or destroyed;As a branch of covert communication,image steganography can achieve covert transmission of secret images.Aiming at the problems of limited hidden capacity and low image security and extracted image quality of the current color image steganography scheme,compressed sensing and image encryption are introduced into image stegantography.In addition,with the development of deep learning,the application of convolutional neural networks to image steganography has gradually become a research focus.Therefore,this paper studies image steganography based on compression encryption and image steganography based on deep learning.The main research content and innovation points of this article include:(1)An S-box design scheme based on hyperchaotic systems and optimization algorithms is proposed.Aiming at the problem of poor chaotic performance of one-dimensional chaotic maps,a new two-dimensional hyperchaotic system 2D-CScubic(2D-Cosine-Sine-Cubic)is obtained by introducing a sine cosine function and an exponential factor,and expanding onedimensional to two-dimensional through coupling ideas.Compared with one-dimensional chaotic maps,2D-CScubic has enhanced pseudorandomness,and has a larger chaotic range and more continuous chaotic intervals.Subsequently,an optimization algorithm for S-boxes,GAPSO-HC(Genetic Algorithm Particle Swarm Optimization Hill Clipping)was designed.This algorithm combines genetic algorithm and particle swarm optimization,and introduces an S-box local optimization strategy based on two-point mountain climbing algorithm to enhance the adaptability of the algorithm for S-box optimization.In order to improve the diversity of the optimization algorithm population,chaotic systems are used to batch construct S-boxes as the initial population of the optimization algorithm.The results show that the overall cryptographic performance of chaotic S-boxes has been effectively improved after optimization.It can be used in image compression and encryption.(2)An image steganography scheme based on compression encryption and local texture difference is proposed.Firstly,in order to improve the quality of secret image reconstruction,two-dimensional compression sensing technology based on chaotic measurement matrix is studied.Next,two-dimensional compression sensing is used for secret image compression and pre encryption,and the compressed image is diffused and encrypted through 2D-CScubic chaotic system and S-box replacement to further ensure the security of the secret image content.Finally,in order to balance the robustness of steganography and visual security,a wavelet domain LSB(Least Significant Bit)embedding algorithm is introduced to implement adaptive embedding of secret images by introducing local texture difference analysis of carrier images.At the same time,S-boxes are used to scramble the embedding position during the embedding process of secret images.Experiments show that the proposed scheme can improve the visual security of the encrypted image and the visual quality of the extracted image while ensuring robustness.(3)An image steganography scheme based on MWCNN network with residual structure is proposed.Considering the information loss problem caused by pooling of convolutional neural networks and the advantage of wavelet domain steganography,Multi-Level Wavelet Convolutional Neural Networks(MWCNN)is used as the framework of image hiding network,and the wavelet transform is used for downsampling to alleviate the information loss caused by pooling,and transforming steganography from the spatial domain to the frequency domain.At the same time,wavelet transform is used to obtain different frequency components of the feature map,and the image detail information is supplemented by wavelet high-frequency component skip fusion,which is combined with skip connection to achieve multi-scale information supplementation.Finally,the residual structure is introduced in the hidden network to optimize the network learning.The experiments show that the proposed scheme can further improve the quality of extracted images on the basis of ensuring the visual security of the carrier image.