Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding capacity.Therefore,this paper innovati...Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding capacity.Therefore,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing technology.Firstly,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space.In the data hiding phase,each encrypted image is sent to a data hider to embed the secret information independently.When r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be extracted.Performance evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and separability.The last but the most important,it has higher embedding capacity.For 512×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.’s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.展开更多
To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according t...To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.展开更多
Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block ...Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62272478 and 61872384)National Natural Science Foundation Youth Foundation Project(Nos.62102451 and 62102450)Basic Frontier Research Foundation Project of Armed Police Engineering University(Nos.WJY202012 and WJY202112).
文摘Until now,some reversible data hiding in encrypted images(RDH-EI)schemes based on secret sharing(SIS-RDHEI)still have the problems of not realizing diffusivity and high embedding capacity.Therefore,this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit(MSB)prediction with secret sharing technology.Firstly,adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space.In the data hiding phase,each encrypted image is sent to a data hider to embed the secret information independently.When r copies of the image carrying the secret text are collected,the original image can be recovered lossless and the secret information can be extracted.Performance evaluation shows that the proposed method in this paper has the diffusivity,reversibility,and separability.The last but the most important,it has higher embedding capacity.For 512×512 grayscale images,the average embedding rate reaches 4.7358 bits per pixel(bpp).Compared to the average embedding rate that can be achieved by the Wang et al.’s SIS-RDHEI scheme,the proposed scheme with(2,2),(2,3),(2,4),(3,4),and(3,5)-threshold can increase by 0.7358 bpp,2.0658 bpp,2.7358 bpp,0.7358 bpp,and 1.5358 bpp,respectively.
基金supported by the National Natural Science Foundation of China(Nos.62272478,61872384,and 62102451)the Basic Frontier Research Foundation of Engineering University of PAP,China(Nos.WJY202012 and WJY202112)。
文摘To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.
基金Project supported by the Scientific Research Project of Liaoning Provincial Department of Education,China(No.JYTMS20231039)the Liaoning Provincial Educational Science Planning Project,China(No.JG22CB252)。
文摘Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.
