At present,the image steganography method based on CNN has achieved good results.The trained model and its parameters are of great value.Once leaked,the secret image will be exposed.To protect the security of steganog...At present,the image steganography method based on CNN has achieved good results.The trained model and its parameters are of great value.Once leaked,the secret image will be exposed.To protect the security of steganographic network model parameters in the transmission process,an idea based on network model parameter scrambling is proposed in this paper.Firstly,the sender trains the steganography network and extraction network,encrypts the extraction network parameters with the key shared by the sender and the receiver,then sends the extraction network and parameters to the receiver through the public channel,and the receiver recovers them with the key after receiving,to achieve more secure secret communication.In this way,even if the network parameters are intercepted by a third party in the transmission process,the interceptor cannot extract the real secret information.In this paper,the classical Joseph algorithm is used as the scrambling algorithm to scramble the extracted network model parameters of the StegoPNet steganography network.The experimental results show that when the scrambled parameters are used for secret image extraction,a meaningless image independent of the secret image is extracted,it shows that this method can well protect the security of steganography network model.At the same time,this method also has good scalability,and can use a variety of different scrambling algorithms to scramble the parameters.展开更多
To enhance information security during transmission over public channels,images are frequently employed for binary data hiding.Nonetheless,data are vulnerable to distortion due to Joint Photographic Experts Group(JPEG...To enhance information security during transmission over public channels,images are frequently employed for binary data hiding.Nonetheless,data are vulnerable to distortion due to Joint Photographic Experts Group(JPEG)compression,leading to challenges in recovering the original binary data.Addressing this issue,this paper introduces a pioneering method for binary data hiding that leverages a combined spatial and channel attention Transformer,termed SCFformer,to withstand JPEG compression.This method employs a novel discrete cosine transform(DCT)quantization truncation mechanism during the hiding phase to bolster the stego image’s resistance to JPEG compression,using spatial and channel attention to conceal information in less perceptible areas,thereby enhancing the model’s resistance to steganalysis.In the extraction phase,the DCT quantization minimizes secret image loss during compression,facilitating easier information retrieval.The incorporation of scalable modules adds flexibility,allowing for variable-capacity data hiding.Experimental findings validate the high security,large capacity,and high flexibility of our scheme,alongside a marked improvement in binary data recovery post-JPEG compression,underscoring our method’s leading-edge performance.展开更多
基金This work was supported in part by the National Natural Science Foundation of China under Grant 62172280,U1904123 and U20B2051the Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province,Henan,China.
文摘At present,the image steganography method based on CNN has achieved good results.The trained model and its parameters are of great value.Once leaked,the secret image will be exposed.To protect the security of steganographic network model parameters in the transmission process,an idea based on network model parameter scrambling is proposed in this paper.Firstly,the sender trains the steganography network and extraction network,encrypts the extraction network parameters with the key shared by the sender and the receiver,then sends the extraction network and parameters to the receiver through the public channel,and the receiver recovers them with the key after receiving,to achieve more secure secret communication.In this way,even if the network parameters are intercepted by a third party in the transmission process,the interceptor cannot extract the real secret information.In this paper,the classical Joseph algorithm is used as the scrambling algorithm to scramble the extracted network model parameters of the StegoPNet steganography network.The experimental results show that when the scrambled parameters are used for secret image extraction,a meaningless image independent of the secret image is extracted,it shows that this method can well protect the security of steganography network model.At the same time,this method also has good scalability,and can use a variety of different scrambling algorithms to scramble the parameters.
基金Project supported by the National Natural Science Foundation of China(Nos.U1904123,62172280,and U20B2051)the Key Scientific Research Projects of Colleges and Universities in Henan Province,China(No.23A520006)the Henan Provincial Science and Technology Research Project,China(No.222102210199)。
文摘To enhance information security during transmission over public channels,images are frequently employed for binary data hiding.Nonetheless,data are vulnerable to distortion due to Joint Photographic Experts Group(JPEG)compression,leading to challenges in recovering the original binary data.Addressing this issue,this paper introduces a pioneering method for binary data hiding that leverages a combined spatial and channel attention Transformer,termed SCFformer,to withstand JPEG compression.This method employs a novel discrete cosine transform(DCT)quantization truncation mechanism during the hiding phase to bolster the stego image’s resistance to JPEG compression,using spatial and channel attention to conceal information in less perceptible areas,thereby enhancing the model’s resistance to steganalysis.In the extraction phase,the DCT quantization minimizes secret image loss during compression,facilitating easier information retrieval.The incorporation of scalable modules adds flexibility,allowing for variable-capacity data hiding.Experimental findings validate the high security,large capacity,and high flexibility of our scheme,alongside a marked improvement in binary data recovery post-JPEG compression,underscoring our method’s leading-edge performance.
