Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completel...Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.展开更多
Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been i...Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been implemented to represent the chemical structure in machine learning models,among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in 2018.However,for small datasets,the use of chemical structure representations typically increases the dimensionality of the input dataset,resulting in a decrease in model performance.Furthermore,the limited diversity of polymer chemical structures hinders the training of reliable embeddings,necessitating complex task-specific architecture implementations.To address these challenges,we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of polymers.This study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 polymers.The results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.展开更多
Metallic nanoparticle （NP） shapes have a significant influence on the property of composite embedded with metallic NPs. Swift heavy ion irradiation is an effective way to modify shapes of metallic NPs embedded in an...Metallic nanoparticle （NP） shapes have a significant influence on the property of composite embedded with metallic NPs. Swift heavy ion irradiation is an effective way to modify shapes of metallic NPs embedded in an amorphous matrix. We investigate the shape deformation of Ag NPs with irradiation fluence, and 357 MeV Ni ions are used to irradiate the silica containing Ag NPs, which are prepared by ion implantation and vacuum annealing. The UV-vis results show that the surface plasmon resonance （SPR） peak from Ag NPs shifts from 400 to 377nm. The SPR peak has a significant shift at fluence lower than 1 × 10^14 ions/cm2 and shows less shift at fluence higher than 1 × 10^14 ions/cm2. The TEM results reveal that the shapes of Ag NPs also show significant deformation at fluence lower than 1 × 10^14 ions/cm2 and show less deformation at fluence higher than 1 × 10^14 ions/cm2. The blue shift of the SPR peak is considered to be the consequence of defect production and Ag NP shape deformation, Based on the thermal spike model calculation, the temperature of the silica surrounding Ag particles first increases rapidly, then the region of Ag NPs close to the interface of Ag/silica is gradually heated. Therefore, the driven force of Ag NPs deformation is considered as the volume expansion of the first heated silica layer surrounding Ag NPs.展开更多
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
文摘Distortion-free data embedding is a technique which can assure that not only the secret data is correctly extracted but also the cover media is recovered without any distortion after secret data is extracted completely. Because of these advantages, this technique attracts the attention of many researchers. In this paper, a new distortion-free data embedding scheme for high dynamic range （HDR） images is proposed. By depending on Cartesian product, this scheme can obtain higher embedding capacity while maintaining the exactly identical cover image and stego image when using the tone mapping algorithms. In experimental results, the proposed scheme is superior to Yu et aL＇s scheme in regard to the embedding rate——an average embedding rate of 0.1355 bpp compared with Yn et aL＇s scheme （0.1270 bpp）.
基金the framework of the program of state support for the centers of the National Technology Initiative(NTI)on the basis of educational institutions of higher education and scientific organizations(Center NTI"Digital Materials Science:New Materials and Substances"on the basis of the Bauman Moscow State Technical University).
文摘Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been implemented to represent the chemical structure in machine learning models,among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in 2018.However,for small datasets,the use of chemical structure representations typically increases the dimensionality of the input dataset,resulting in a decrease in model performance.Furthermore,the limited diversity of polymer chemical structures hinders the training of reliable embeddings,necessitating complex task-specific architecture implementations.To address these challenges,we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of polymers.This study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 polymers.The results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11475230 and U1532262
文摘Metallic nanoparticle （NP） shapes have a significant influence on the property of composite embedded with metallic NPs. Swift heavy ion irradiation is an effective way to modify shapes of metallic NPs embedded in an amorphous matrix. We investigate the shape deformation of Ag NPs with irradiation fluence, and 357 MeV Ni ions are used to irradiate the silica containing Ag NPs, which are prepared by ion implantation and vacuum annealing. The UV-vis results show that the surface plasmon resonance （SPR） peak from Ag NPs shifts from 400 to 377nm. The SPR peak has a significant shift at fluence lower than 1 × 10^14 ions/cm2 and shows less shift at fluence higher than 1 × 10^14 ions/cm2. The TEM results reveal that the shapes of Ag NPs also show significant deformation at fluence lower than 1 × 10^14 ions/cm2 and show less deformation at fluence higher than 1 × 10^14 ions/cm2. The blue shift of the SPR peak is considered to be the consequence of defect production and Ag NP shape deformation, Based on the thermal spike model calculation, the temperature of the silica surrounding Ag particles first increases rapidly, then the region of Ag NPs close to the interface of Ag/silica is gradually heated. Therefore, the driven force of Ag NPs deformation is considered as the volume expansion of the first heated silica layer surrounding Ag NPs.
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.
