With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep...With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods,however both of which have their limitations.The former is difcult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm.Due to the irreversibility of the process of generating secret images from message codeword,the recovery accuracy of the latter is very poor.To this end,this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography(JoCS).Firstly,this paper proposes the Semantic Factorization Fitting module(SeFF)and the Transform Domain Steganography module(TrDS).The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network,which establishs a mapping rule between message codeword and the coarse feature of the generated image,and then the extractor is used to ft the above mapping rule,which has excellent robustness and completeness;the latter encodes the main content area of the image based on the encoder in VQGAN,and then adds secret message to the latent vector of the encoded image,which achieves the steganography in the latent domain of the image.Secondly,we demonstrate the independence between two modules and the advantages of connecting two modules.By using the image generated in the SeFF module as the cover image in the TrDS module,secondary steganography of a single image is achieved,based on which we design the JoCS scheme.The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100%completeness,excellent image quality and outstanding robustness against various image attacks.Moreover,our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication.Finally,this paper also discusses the following three points as further elaboration of the scheme:(1)the advantages of the mapping rule in the SeFF module(2)the verifcation of the independence between the two modules(3)the fexibility of the joint steganography scheme.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant U23B2002in part by the National Natural Science Foundation of China under Grant 62272007.
文摘With the development of deep learning technology,great progress has been made in the feld of coverless steganography based on deep learning technology,including some selection-based steganography methods that use deep learning technology and all generation-based steganography methods,however both of which have their limitations.The former is difcult to meet actual communication requirements in terms of communication capacity and completeness due to the limit of the algorithm.Due to the irreversibility of the process of generating secret images from message codeword,the recovery accuracy of the latter is very poor.To this end,this paper designs a robust joint coverless image steganography scheme called Joint Coverless Image Steganography(JoCS).Firstly,this paper proposes the Semantic Factorization Fitting module(SeFF)and the Transform Domain Steganography module(TrDS).The former adds the secret message to the input vector of the low resolution layer in the StyleGAN generator network,which establishs a mapping rule between message codeword and the coarse feature of the generated image,and then the extractor is used to ft the above mapping rule,which has excellent robustness and completeness;the latter encodes the main content area of the image based on the encoder in VQGAN,and then adds secret message to the latent vector of the encoded image,which achieves the steganography in the latent domain of the image.Secondly,we demonstrate the independence between two modules and the advantages of connecting two modules.By using the image generated in the SeFF module as the cover image in the TrDS module,secondary steganography of a single image is achieved,based on which we design the JoCS scheme.The results show that our scheme breaks through the communication capacity limit in the selection-based coverless methods while guaranteeing 100%completeness,excellent image quality and outstanding robustness against various image attacks.Moreover,our scheme exhibits strong security against detection by multiple steganalysis tools and excellent practicality in practical communication.Finally,this paper also discusses the following three points as further elaboration of the scheme:(1)the advantages of the mapping rule in the SeFF module(2)the verifcation of the independence between the two modules(3)the fexibility of the joint steganography scheme.
