With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multi...With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.展开更多
Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of...Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of only two regions:the focal and nonfocal regions.The focal region mainly contains information for diagnosis,while the nonfocal region serves as the monochrome background.The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images,and manual segmentation is time-consuming,poorly reproducible,and operator-dependent.Implementing state-of-the-art deep learning(DL)models will facilitate key benefits,but the lack of domain-specific labels for existing medical datasets makes it impossible.To address this problem,this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this domain.First,an initial segmentation based on a 33 kernel is performed to analyze×identified contour pixels before classifying pixels into focal and nonfocal regions.Then,several human expert raters evaluate and classify the generated labels into accurate and inaccurate labels.The inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed dataset.To ensure reliability and integrity in the proposed dataset,we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics:dice coefficient,Jaccard index,precision,recall,and accuracy.The experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts,with an average accuracy score of 94%and dice coefficient scores between 90%-99%.The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation(scSE)architecture for semantic segmentation to validate and illustrate the usefulness of the proposed dataset.The results demonstrate the superior performance of the proposed architecture in accurately separating the focal and nonfocal regions compared to state-of-the-art architectures.Dataset information is released under the following URL:https://www.kaggle.com/lordamoah/datasets(accessed on 31 March 2025).展开更多
Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full ...Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.展开更多
With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed...With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments.However,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios.To address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction accuracy.The carrier image is subsequently reconstructed using the Huffman coding technique.Encrypted image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE scheme.Ultimately,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted image.This approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled access.Test results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image recovery.Specifically,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD databases.Furthermore,the algorithm facilitates permission-granting and joint-decryption capabilities.Additionally,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pixel change rate(NPCR),confirming its high level of security.Overall,the algorithm can be applied in a multi-user and multi-level cloud service environment to realize the secure storage of carrier images and secret data.展开更多
Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement...Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement)algorithm is proposed as an automatic contrast enhancement(CE)method for medical images based on region ofinterest(ROI)and non-region of interest(NROI).The PRDHMCE algorithm strategically enhances the ROI aftersegmentation using histogram stretching and data embedding.An initial histogram evaluation compares histogrambins with their neighbours to select the bin with the maximum pixel count.The selected bin is set as the point forcontrast stretching with enhancement and secret data embedding in the ROI.The remaining data is embedded inthe NROIwhile reducing image distortions.Experimentalresultsshowthe effectiveness of PRDHMCE in optimallyimproving image contrast and increasing embedding capacity comparedwith existing methods based on qualitativeand objective metricssuch as peak signal-to-noise ratio(PSNR),structuralsimilarity index(SSIM),relative contrasterror(RCE),relative mean brightness error(RMBE)and mean opinion score(MOS).Additionally,PRDHMCErecovers medical images fully without data loss.展开更多
Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Base...Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Based on the relationships between pixels and their neighbors, we propose a reversible data embedding scheme to embed hidden messages into an original image. In our proposed scheme, a two-layer data embedding approach is used for our proposed data embedding phase. Layer-1 embedding is used to hide secret data. Layer-2 embedding, which is an embedding variant of the proposed layer-1 embedding, is used to hide side information, such as the parameters required to restore the marked image. In our layer-1 embedding, the value of an embedded pixel is determined according to a predetermined threshold and the relationship between the pixel and its neighbors. In our layer-2 embedding, the similar data embedding concept is expanded to the block-based. Experimental results provide supportive data to show that the proposed scheme can provide greater hiding capacity while preserving the image quality of a marked image in comparison with previous work.展开更多
The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they ofte...The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.展开更多
Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In...Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.展开更多
Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the clou...Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.展开更多
Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy protection.In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encr...Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy protection.In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images,we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling.First,the prediction error image is obtained by a novel prediction method before encryption.Then,the image pixel values are divided into two categories by the threshold range,which is selected adaptively according to the image content.Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged.The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm.Moreover,the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position.Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-of-the-art methods for images with different texture complexity.展开更多
Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on blo...Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on block classification and permutation is proposed.Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively.After block classification,content owner utilizes a specific encryption method,including stream cipher encryption and block permutation to protect image content securely.For the encrypted image,data hider embeds additional secret information in the most significant bits(MSB)of the encrypted pixels in smooth blocks and the final marked image can be obtained.At the receiver side,secret data will be extracted correctly with data-hiding key.When receiver only has encryption key,after stream cipher decryption,block scrambling decryption and MSB error prediction with threshold,decrypted image will be achieved.When data hiding key and encryption key are both obtained,receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly,hence,receiver can recover marked image losslessly.Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.展开更多
Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In dat...Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.展开更多
Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the ph...Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks （DCNs）. To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm （TAE） and first fit virtual link mapping （FFLM） to map virtual data center requests to a physical data center. Simulation results show that TAE＋FFLM could increase acceptance rate and reduce network cost （about 49% in the case） at the same time. The traffie aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.展开更多
A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided ...A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.展开更多
To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether ...To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image.However,conventional steganalysis schemes focused on the previous RDH algorithms,i.e.,some early spatial/pixel domain-based histogram-shifting(HS)schemes,which might cause great changes in statistical characteristics and thus be easy to be detected.For recent improved methods,such as some adaptive prediction error(PE)based embedding schemes,those conventional schemes might be invalid,since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis.Therefore,a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH.The contributions of this paper could be summarized as follows.(1)By analyzing the characteristics for those adaptive HS-RDH,an effective“flat ground”based detection method is designed to fast identify whether the given image is used to hide secret data;(2)According to the empirical statistical model,double check mechanism is provided to improve the detection accuracy;(3)In addition,to further improve detection ability,some detailed information for secret data,i.e.,its content and embedding location are further estimated.Compared with conventional steganalysis methods,experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.展开更多
With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding perf...With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.展开更多
In order to achieve image encryption and data embedding simultaneously, a reversible data hiding(RDH) algorithm for encrypted-compressed image in wavelet domain is proposed. This scheme employs the quality controllabl...In order to achieve image encryption and data embedding simultaneously, a reversible data hiding(RDH) algorithm for encrypted-compressed image in wavelet domain is proposed. This scheme employs the quality controllable parameter. Moreover it has larger embedding capacity and smaller quality control parameters than other methods in literatures. Meanwhile, the cross chaotic map is employed to generate chaotic sequences, and the total keys of the algorithm is far large. Experimental results and comparisons show that the proposed scheme has large capacity, high security, and strong resistance to brute-force.展开更多
Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction dur...Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.展开更多
An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capa...An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.展开更多
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
基金funded by theNational Science and Technology Council of Taiwan under the grant number NSTC 113-2221-E-035-058.
文摘With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.
基金supported by the National Natural Science Foundation of China(Grant Nos.62072250,61772281,61702235,U1636117,U1804263,62172435,61872203 and 61802212)the Zhongyuan Science and Technology Innovation Leading Talent Project of China(Grant No.214200510019)+3 种基金the Suqian Municipal Science and Technology Plan Project in 2020(S202015)the Plan for Scientific Talent of Henan Province(Grant No.2018JR0018)the Opening Project of Guangdong Provincial Key Laboratory of Information Security Technology(Grant No.2020B1212060078)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Medical image segmentation,i.e.,labeling structures of interest in medical images,is crucial for disease diagnosis and treatment in radiology.In reversible data hiding in medical images(RDHMI),segmentation consists of only two regions:the focal and nonfocal regions.The focal region mainly contains information for diagnosis,while the nonfocal region serves as the monochrome background.The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images,and manual segmentation is time-consuming,poorly reproducible,and operator-dependent.Implementing state-of-the-art deep learning(DL)models will facilitate key benefits,but the lack of domain-specific labels for existing medical datasets makes it impossible.To address this problem,this study provides labels of existing medical datasets based on a hybrid segmentation approach to facilitate the implementation of DL segmentation models in this domain.First,an initial segmentation based on a 33 kernel is performed to analyze×identified contour pixels before classifying pixels into focal and nonfocal regions.Then,several human expert raters evaluate and classify the generated labels into accurate and inaccurate labels.The inaccurate labels undergo manual segmentation by medical practitioners and are scored based on a hierarchical voting scheme before being assigned to the proposed dataset.To ensure reliability and integrity in the proposed dataset,we evaluate the accurate automated labels with manually segmented labels by medical practitioners using five assessment metrics:dice coefficient,Jaccard index,precision,recall,and accuracy.The experimental results show labels in the proposed dataset are consistent with the subjective judgment of human experts,with an average accuracy score of 94%and dice coefficient scores between 90%-99%.The study further proposes a ResNet-UNet with concatenated spatial and channel squeeze and excitation(scSE)architecture for semantic segmentation to validate and illustrate the usefulness of the proposed dataset.The results demonstrate the superior performance of the proposed architecture in accurately separating the focal and nonfocal regions compared to state-of-the-art architectures.Dataset information is released under the following URL:https://www.kaggle.com/lordamoah/datasets(accessed on 31 March 2025).
基金supported by the National Natural Science Foundation of China(Nos.92152301,12072282)。
文摘Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.
基金the National Natural Science Foundation of China(Grant Numbers 622724786210245062102451).
文摘With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments.However,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios.To address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction accuracy.The carrier image is subsequently reconstructed using the Huffman coding technique.Encrypted image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE scheme.Ultimately,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted image.This approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled access.Test results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image recovery.Specifically,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD databases.Furthermore,the algorithm facilitates permission-granting and joint-decryption capabilities.Additionally,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pixel change rate(NPCR),confirming its high level of security.Overall,the algorithm can be applied in a multi-user and multi-level cloud service environment to realize the secure storage of carrier images and secret data.
基金supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Contrast enhancement in medical images has been vitalsince the prevalence of image representationsin healthcare.In this research,the PRDHMCE(pairwise reversible data hiding for medical images with contrast enhancement)algorithm is proposed as an automatic contrast enhancement(CE)method for medical images based on region ofinterest(ROI)and non-region of interest(NROI).The PRDHMCE algorithm strategically enhances the ROI aftersegmentation using histogram stretching and data embedding.An initial histogram evaluation compares histogrambins with their neighbours to select the bin with the maximum pixel count.The selected bin is set as the point forcontrast stretching with enhancement and secret data embedding in the ROI.The remaining data is embedded inthe NROIwhile reducing image distortions.Experimentalresultsshowthe effectiveness of PRDHMCE in optimallyimproving image contrast and increasing embedding capacity comparedwith existing methods based on qualitativeand objective metricssuch as peak signal-to-noise ratio(PSNR),structuralsimilarity index(SSIM),relative contrasterror(RCE),relative mean brightness error(RMBE)and mean opinion score(MOS).Additionally,PRDHMCErecovers medical images fully without data loss.
基金supported by the National Science Council Foundation under Grant No.NSC 98-2410-H-126-007-MY3
文摘Reversible data embedding is becoming a very important issue in securing images transmitted over the Internet, especially in dealing with sensitive images such as those created for military data and medical data. Based on the relationships between pixels and their neighbors, we propose a reversible data embedding scheme to embed hidden messages into an original image. In our proposed scheme, a two-layer data embedding approach is used for our proposed data embedding phase. Layer-1 embedding is used to hide secret data. Layer-2 embedding, which is an embedding variant of the proposed layer-1 embedding, is used to hide side information, such as the parameters required to restore the marked image. In our layer-1 embedding, the value of an embedded pixel is determined according to a predetermined threshold and the relationship between the pixel and its neighbors. In our layer-2 embedding, the similar data embedding concept is expanded to the block-based. Experimental results provide supportive data to show that the proposed scheme can provide greater hiding capacity while preserving the image quality of a marked image in comparison with previous work.
文摘The reverse design of solid rocket motor(SRM)propellant grain involves determining the grain geometry to closely match a predefined internal ballistic curve.While existing reverse design methods are feasible,they often face challenges such as lengthy computation times and limited accuracy.To achieve rapid and accurate matching between the targeted ballistic curve and complex grain shape,this paper proposes a novel reverse design method for SRM propellant grain based on time-series data imaging and convolutional neural network(CNN).First,a finocyl grain shape-internal ballistic curve dataset is created using parametric modeling techniques to comprehensively cover the design space.Next,the internal ballistic time-series data is encoded into three-channel images,establishing a potential relationship between the ballistic curves and their image representations.A CNN is then constructed and trained using these encoded images.Once trained,the model enables efficient inference of propellant grain dimensions from a target internal ballistic curve.This paper conducts comparative experiments across various neural network models,validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images,as well as its generalization capability across different CNN architectures.Ignition tests were performed based on the predicted propellant grain.The results demonstrate that the relative error between the experimental internal ballistic curves and the target curves is less than 5%,confirming the validity and feasibility of the proposed reverse design methodology.
文摘Reversible data hiding techniques are capable of reconstructing the original cover image from stego-images. Recently, many researchers have focused on reversible data hiding to protect intellectual property rights. In this paper, we combine reversible data hiding with the chaotic Henon map as an encryption technique to achieve an acceptable level of confidentiality in cloud computing environments. And, Haar digital wavelet transformation （HDWT） is also applied to convert an image from a spatial domain into a frequency domain. And then the decimal of coefficients and integer of high frequency band are modified for hiding secret bits. Finally, the modified coefficients are inversely transformed to stego-images.
基金This work was supported by the National Natural Science Foundation of China(No.61702276)the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology under Grant 2016r055 and the Priority Academic Program Development(PAPD)of Jiangsu Higher Education Institutions.The authors are grateful for the anonymous reviewers who made constructive comments and improvements.
文摘Advanced cloud computing technology provides cost saving and flexibility of services for users.With the explosion of multimedia data,more and more data owners would outsource their personal multimedia data on the cloud.In the meantime,some computationally expensive tasks are also undertaken by cloud servers.However,the outsourced multimedia data and its applications may reveal the data owner’s private information because the data owners lose the control of their data.Recently,this thought has aroused new research interest on privacy-preserving reversible data hiding over outsourced multimedia data.In this paper,two reversible data hiding schemes are proposed for encrypted image data in cloud computing:reversible data hiding by homomorphic encryption and reversible data hiding in encrypted domain.The former is that additional bits are extracted after decryption and the latter is that extracted before decryption.Meanwhile,a combined scheme is also designed.This paper proposes the privacy-preserving outsourcing scheme of reversible data hiding over encrypted image data in cloud computing,which not only ensures multimedia data security without relying on the trustworthiness of cloud servers,but also guarantees that reversible data hiding can be operated over encrypted images at the different stages.Theoretical analysis confirms the correctness of the proposed encryption model and justifies the security of the proposed scheme.The computation cost of the proposed scheme is acceptable and adjusts to different security levels.
基金supported by the National Natural Science Foundation of China(61872303,U1936113)the Science and Technology Innovation Talents Program of Sichuan Science and Technology Department(2018RZ0143)the Key Project of Sichuan Science and Technology Innovation Pioneering Miaozi Project(19MZGC0163).
文摘Reversible data hiding in encrypted images(RDH-EI)technology is widely used in cloud storage for image privacy protection.In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images,we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling.First,the prediction error image is obtained by a novel prediction method before encryption.Then,the image pixel values are divided into two categories by the threshold range,which is selected adaptively according to the image content.Multiple high-significant bits of pixels within the threshold range are used for embedding data and pixel values outside the threshold range remain unchanged.The optimal threshold selected adaptively ensures the maximum embedding capacity of the algorithm.Moreover,the security of encrypted images can be improved by the combination of XOR encryption and classification scrambling encryption since the embedded data is independent of the pixel position.Experiment results demonstrate that the proposed method has higher embedding capacity compared with the current state-of-the-art methods for images with different texture complexity.
基金This work was supported by the National Natural Science Foundation of China(61672354,61702332).
文摘Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on block classification and permutation is proposed.Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively.After block classification,content owner utilizes a specific encryption method,including stream cipher encryption and block permutation to protect image content securely.For the encrypted image,data hider embeds additional secret information in the most significant bits(MSB)of the encrypted pixels in smooth blocks and the final marked image can be obtained.At the receiver side,secret data will be extracted correctly with data-hiding key.When receiver only has encryption key,after stream cipher decryption,block scrambling decryption and MSB error prediction with threshold,decrypted image will be achieved.When data hiding key and encryption key are both obtained,receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly,hence,receiver can recover marked image losslessly.Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.
基金supported in part by the following funding agencies of China:National Natural Science Foundation under Grant 61602050 and U1534201National Key Research and Development Program of China under Grant 2016QY01W0200
文摘Virtual data center is a new form of cloud computing concept applied to data center. As one of the most important challenges, virtual data center embedding problem has attracted much attention from researchers. In data centers, energy issue is very important for the reality that data center energy consumption has increased by dozens of times in the last decade. In this paper, we are concerned about the cost-aware multi-domain virtual data center embedding problem. In order to solve this problem, this paper first addresses the energy consumption model. The model includes the energy consumption model of the virtual machine node and the virtual switch node, to quantify the energy consumption in the virtual data center embedding process. Based on the energy consumption model above, this paper presents a heuristic algorithm for cost-aware multi-domain virtual data center embedding. The algorithm consists of two steps: inter-domain embedding and intra-domain embedding. Inter-domain virtual data center embedding refers to dividing virtual data center requests into several slices to select the appropriate single data center. Intra-domain virtual data center refers to embedding virtual data center requests in each data center. We first propose an inter-domain virtual data center embedding algorithm based on label propagation to select the appropriate single data center. We then propose a cost-aware virtual data center embedding algorithm to perform the intra-domain data center embedding. Extensive simulation results show that our proposed algorithm in this paper can effectively reduce the energy consumption while ensuring the success ratio of embedding.
基金This research was partially supported by the National Grand Fundamental Research 973 Program of China under Grant （No. 2013CB329103）, Natural Science Foundation of China grant （No. 61271171）, the Fundamental Research Funds for the Central Universities （ZYGX2013J002, ZYGX2012J004, ZYGX2010J002, ZYGX2010J009）, Guangdong Science and Technology Project （2012B090500003, 2012B091000163, 2012556031）.
文摘Virtualization is a common technology for resource sharing in data center. To make efficient use of data center resources, the key challenge is to map customer demands （modeled as virtual data center, VDC） to the physical data center effectively. In this paper, we focus on this problem. Distinct with previous works, our study of VDC embedding problem is under the assumption that switch resource is the bottleneck of data center networks （DCNs）. To this end, we not only propose relative cost to evaluate embedding strategy, decouple embedding problem into VM placement with marginal resource assignment and virtual link mapping with decided source-destination based on the property of fat-tree, but also design the traffic aware embedding algorithm （TAE） and first fit virtual link mapping （FFLM） to map virtual data center requests to a physical data center. Simulation results show that TAE＋FFLM could increase acceptance rate and reduce network cost （about 49% in the case） at the same time. The traffie aware embedding algorithm reduces the load of core-link traffic and brings the optimization opportunity for data center network energy conservation.
文摘A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity.During embedding,the image is divided into various 3×3 blocks.Then,using the LBP-based image descriptor,the LBP codes for each block are computed.Next,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process.Further,each cover image(CI)pixel produces two different stego-image pixels.Likewise,during extraction,the CI pixels are restored without the loss of a single bit of information.The outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art techniques.In addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular analysis.Besides,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.
基金This work was supported in part by the National Natural Science Foundation of China under Grant 61762054,U1736215,61772573 and 61563022in part by the National Science Foundation for Distinguished Young Scholars of Jiangxi Province under Grant 20171BCB23072Many thanks to the anonymous reviewers for their insightful comments and valuable suggestions,which helped a lot to improve the paper quality.
文摘To measure the security for hot searched reversible data hiding(RDH)technique,especially for the common-used histogram-shifting based RDH(denoted as HS-RDH),several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image.However,conventional steganalysis schemes focused on the previous RDH algorithms,i.e.,some early spatial/pixel domain-based histogram-shifting(HS)schemes,which might cause great changes in statistical characteristics and thus be easy to be detected.For recent improved methods,such as some adaptive prediction error(PE)based embedding schemes,those conventional schemes might be invalid,since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis.Therefore,a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH.The contributions of this paper could be summarized as follows.(1)By analyzing the characteristics for those adaptive HS-RDH,an effective“flat ground”based detection method is designed to fast identify whether the given image is used to hide secret data;(2)According to the empirical statistical model,double check mechanism is provided to improve the detection accuracy;(3)In addition,to further improve detection ability,some detailed information for secret data,i.e.,its content and embedding location are further estimated.Compared with conventional steganalysis methods,experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.
文摘With the reversible data hiding method based on pixel-value-ordering,data are embedded through the modification of the maximum and minimum values of a block.A significant relationship exists between the embedding performance and the block size.Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data;the smaller the pixel blocks,greater is the embedding capacity.However,it tends to result in the deterioration of the quality of the marked image.Herein,a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.’s pixel-value-ordering method,which realizes the dynamic control of block size by considering the image texture.First,the cover image is divided into non-overlapping 2×2 pixel blocks.Subsequently,according to their complexity,similarity and thresholds,these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4,4×2,or 4×4 sized blocks.Hence,smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio.Experimental results prove that the proposed method is superior to the other three advanced methods.It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.
基金Supported by the Chongqing Research Program of Basic Research and Frontier Technology(cstc2017jcyjBX0008)the Graduate Student Research and Innovation Foundation of Chongqing(CYB17026)the Basic Applied Research Program of Qinghai Province(2019-ZJ-7099)
文摘In order to achieve image encryption and data embedding simultaneously, a reversible data hiding(RDH) algorithm for encrypted-compressed image in wavelet domain is proposed. This scheme employs the quality controllable parameter. Moreover it has larger embedding capacity and smaller quality control parameters than other methods in literatures. Meanwhile, the cross chaotic map is employed to generate chaotic sequences, and the total keys of the algorithm is far large. Experimental results and comparisons show that the proposed scheme has large capacity, high security, and strong resistance to brute-force.
基金This work was supported in part by the Natural Science Foundation of Hunan Province(No.2020JJ4141),author X.X,http://kjt.hunan.gov.cn/in part by the Postgraduate Excellent teaching team Project of Hunan Province under Grant(No.ZJWKT202204),author J.Q,http://zfsg.gd.gov.cn/xxfb/ywsd/index.html.
文摘Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.
文摘An outsource database is a database service provided by cloud computing companies.Using the outsource database can reduce the hardware and software＇s cost and also get more efficient and reliable data processing capacity.However,the outsource database still has some challenges.If the service provider does not have sufficient confidence,there is the possibility of data leakage.The data may has user＇s privacy,so data leakage may cause data privacy leak.Based on this factor,to protect the privacy of data in the outsource database becomes very important.In the past,scholars have proposed k-anonymity to protect data privacy in the database.It lets data become anonymous to avoid data privacy leak.But k-anonymity has some problems,it is irreversible,and easier to be attacked by homogeneity attack and background knowledge attack.Later on,scholars have proposed some studies to solve homogeneity attack and background knowledge attack.But their studies still cannot recover back to the original data.In this paper,we propose a data anonymity method.It can be reversible and also prevent those two attacks.Our study is based on the proposed r-transform.It can be used on the numeric type of attributes in the outsource database.In the experiment,we discussed the time required to anonymize and recover data.Furthermore,we investigated the defense against homogeneous attack and background knowledge attack.At the end,we summarized the proposed method and future researches.
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）.
