High-Decoding Accuracy Image Steganography Based on GAN and Separated Convolution
The problem of low decoding accuracy and image visual quality,long encoding and decoding time are in existing image steganogra-phy.In view of the above challenges,a high-decoding accuracy image steganography based on GAN and separated convolution is proposed.An Residual-Rep structure-based and Inception-SCS structure-based preprocessing network is used to automatically learn the high-dimen-sional features of the image and use the feature representation in a data-driven way before embedding the secret information,acquiring feature information for both channels and spaces,and the skipping connection is used to reduce the loss of secret information,and reduce model com-plexity by shortening encoding and decoding time.In order to improve the dense decoder's accuracy,the error correction layer,error correc-tion function and Wasserstein distance are introduced.In a typical environment,an average decoding accuracy of 0.89 and an average structur-al similarity of 0.95 are obtained,which improves the decoding accuracy and reduces image distortion.The encoding time is reduced by half compared to both SteganoGAN and Hidden methods,allowing the encoding task to be completed in a shorter time.
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