Coverless Steganography for Digital Images Based on a Generative Model

In this paper,we propose a novel coverless image steganographic scheme based on a generative model.In our scheme,the secret image is first fed to the generative model database,to generate a meaning-normal and independent image different from the secret image.The generated image is then transmitted to the receiver and fed to the generative model database to generate another image visually the same as the secret image.Thus,we only need to transmit the meaning-normal image which is not related to the secret image,and we can achieve the same effect as the transmission of the secret image.This is the first time to propose the coverless image information steganographic scheme based on generative model,compared with the traditional image steganography.The transmitted image is not embedded with any information of the secret image in this method,therefore,can effectively resist steganalysis tools.Experimental results show that our scheme has high capacity,security and reliability.

Server-Aided Multi-Secret Sharing Scheme for Weak Computational Devices

In the setting of(t,n)threshold secret sharing,at least t parties can reconstruct the secret,and fewer than t parties learn nothing about the secret.However,to achieve fairness,the existing secret sharing schemes either assume a trusted party exists or require running multi-round,which is not practical in a real application.In addition,the cost of verification grows dramatically with the number of participants and the communication complexity is O(t),if there is not a trusted combiner in the reconstruction phase.In this work,we propose a fair server-aided multi-secret sharing scheme for weak computational devices.The malicious behavior of clients or server providers in the scheme can be verified,and the server provider learns nothing about the secret shadows and the secrets.Unlike other secret sharing schemes,our scheme does not require interaction among users and can work in asynchronous mode,which is suitable for mobile networks or cloud computing environments since weak computational mobile devices are not always online.Moreover,in the scheme,the secret shadow is reusable,and expensive computation such as reconstruction computation and homomorphic verification computation can be outsourced to the server provider,and the users only require a small amount of computation.

Image Information Hiding Method Based on Image Compression and Deep Neural Network

Image steganography is a technique that hides secret information into the cover image to protect information security.The current image steganography is mainly to embed a smaller secret image in an area such as a texture of a larger-sized cover image,which will cause the size of the secret image to be much smaller than the cover image.Therefore,the problem of small steganographic capacity needs to be solved urgently.This paper proposes a steganography framework that combines image compression.In this framework,the Vector Quantized Variational AutoEncoder(VQ-VAE)is used to achieve the compression of the secret image.The compressed and reconstructed image is visually indistinguishable from the original image and facilitates more embedded data information later.Finally,the compressed image is transmitted to a SegNet deep neural network that contains a set of encoders and decoders to achieve image hiding and extraction.Experimental results show that the steganographic framework guarantees the quality of steganography while its relative steganographic capacity reaches 1.Besides,Peak Signal-to-Noise Ratio(PSNR)and Structural Similarity Index(SSIM)values can reach 42 dB and 0.94,respectively.

A Steganography Model Data Protection Method Based on Scrambling Encryption

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.