Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation env...Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.展开更多
Network security situation awareness is an important foundation for network security management,which presents the target system security status by analyzing existing or potential cyber threats in the target system.In...Network security situation awareness is an important foundation for network security management,which presents the target system security status by analyzing existing or potential cyber threats in the target system.In network offense and defense,the network security state of the target system will be affected by both offensive and defensive strategies.According to this feature,this paper proposes a network security situation awareness method using stochastic game in cloud computing environment,uses the utility of both sides of the game to quantify the network security situation value.This method analyzes the nodes based on the network security state of the target virtual machine and uses the virtual machine introspection mechanism to obtain the impact of network attacks on the target virtual machine,then dynamically evaluates the network security situation of the cloud environment based on the game process of both attack and defense.In attack prediction,cyber threat intelligence is used as an important basis for potential threat analysis.Cyber threat intelligence that is applicable to the current security state is screened through the system hierarchy fuzzy optimization method,and the potential threat of the target system is analyzed using the cyber threat intelligence obtained through screening.If there is no applicable cyber threat intelligence,using the Nash equilibrium to make predictions for the attack behavior.The experimental results show that the network security situation awareness method proposed in this paper can accurately reflect the changes in the network security situation and make predictions on the attack behavior.展开更多
Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detectio...Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.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
With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature t...With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.展开更多
In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on...In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.展开更多
A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources ...A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.展开更多
As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on colle...As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy threats.To address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA.Specifically,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart grids.Subsequently,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous authentication.In particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation.Finally,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.展开更多
Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography.Normally,the traditional way in designing a trapdoor is to identify a computationally har...Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography.Normally,the traditional way in designing a trapdoor is to identify a computationally hard problem,such as the NPC problems.So the trapdoor in a public key encryption mechanism turns out to be a type of limited resource.In this paper,we generalize the methodology of adversarial learning model in artificial intelligence and introduce a novel way to conveniently obtain sub-optimal and computationally hard trapdoors based on the automatic information theoretic search technique.The basic routine is constructing a generative architecture to search and discover a probabilistic reversible generator which can correctly encoding and decoding any input messages.The architecture includes a trapdoor generator built on a variational autoencoder(VAE)responsible for searching the appropriate trapdoors satisfying a maximum of entropy,a random message generator yielding random noise,and a dynamic classifier taking the results of the two generator.The evaluation of our construction shows the architecture satisfying basic indistinguishability of outputs under chosen-plaintext attack model(CPA)and high efficiency in generating cheap trapdoors.展开更多
In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite netw...In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.展开更多
Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a sin...Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its availability.Recently,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et al.Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset.In this paper,firstly,an efficient attack with both theorems proving and experimentative verification is launched.We find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the SM.Secondly,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption process.In addition,fault tolerance is very important to aggregation schemes in practical scenarios.In most of the current schemes,once some SMs failed,then they will not work.As far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance.Finally,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts.Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.展开更多
Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges ...Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.展开更多
This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark int...This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.展开更多
With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in...With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground networks.However,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data transmission.In addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal routes.The service quality requirements cannot be satisfied when multiple service requests are made.Based on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real time.Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability.Compared with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics.展开更多
Recently, security in embedded system arises attentions because of modern electronic devices need cau- tiously either exchange or communicate with the sensitive data. Although security is classical research topic in...Recently, security in embedded system arises attentions because of modern electronic devices need cau- tiously either exchange or communicate with the sensitive data. Although security is classical research topic in world- wide communication, the researchers still face the problems of how to deal with these resource constraint devices and en- hance the features of assurance and certification. Therefore, some computations of cryptographic algorithms are built on hardware platforms, such as field program gate arrays （FPGAs）. The commonly used cryptographic algorithms for digital signature algorithm （DSA） are rivest-shamir-adleman （RSA） and elliptic curve cryptosystems （ECC） which based on the presumed difficulty of factoring large integers and the algebraic structure of elliptic curves over finite fields. Usu- ally, RSA is computed over GF（p）, and ECC is computed over GF（p） or GF（2P）. Moreover, embedded applications need advance encryption standard （AES） algorithms to pro- cess encryption and decryption procedures. In order to reuse the hardware resources and meet the trade-off between area and performance, we proposed a new triple functional arith- metic unit for computing high radix RSA and ECC operations over GF（p） and GF（2P）, which also can be extended to support AES operations. A new high radix signed digital （SD） adder has been proposed to eliminate the carry propagations over GF（p）. The proposed unified design took up 28.7% less hardware resources than implementing RSA, ECC, and AES individually, and the experimental results show that our proposed architecture can achieve 141.8 MHz using approxi- mately 5.5k CLBs on Virtex-5 FPGA.展开更多
H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography.In this paper,a novel motion vector(MV)based steganographic algorithm is proposed for the H.264/AVC...H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography.In this paper,a novel motion vector(MV)based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion.Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm.In the embedding block,the motion vector space encoding is used to embed a(2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block.Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding.The re-motion compensation(re-MC)block will re-construct the macroblock(MB)whose motion vectors have been modified by embedding block.System block plays the role of the generator for chaotic sequences and encryptor for secret data.Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion,it also presents good undetectability for existing MV-based steganalysis feature.Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.展开更多
Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system comp...Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.展开更多
Aiming at the security problem of range gated laser imaging in high noise background,a range gated laser image encryption scheme based on the quantum genetic algorithm(QGA)is proposed.Due to the fuzziness of the laser...Aiming at the security problem of range gated laser imaging in high noise background,a range gated laser image encryption scheme based on the quantum genetic algorithm(QGA)is proposed.Due to the fuzziness of the laser image itself,the randomness and security of the key become more and more important in encryption.In this paper,the chaotic sequence is used as the parent chromosome of the QGA,and the random number satisfying the encryption algorithm is obtained by an iterative genetic algorithm.To further improve the security of laser images,some random pixels are stochastically inserted around the laser image before scrambling.These random pixels are scrambled together with the image.Finally,an adaptive diffusion method is designed to completely change the original statistical information of the image.Experimental simulation and performance analysis show that the scheme has high security.展开更多
Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal ...Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal services even when certain proxy servers are paralyzed or damaged.A non-interactive identity-based TPRE(IB-TPRE)scheme over lattices is proposed which removes the public key certificates.To accomplish this scheme,Shamir’s secret sharing is employed twice,which not only effectively hides the delegator’s private key information,but also decentralizes the proxy power by splitting the re-encryption key.Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share.This property is achieved by lattice-based fully homomorphic signatures.As a result,the whole scheme is thoroughly capable of resisting quantum attacks even when they are available.The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model.Two typical application scenarios,including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography,are presented.展开更多
The existing image encryption schemes are not suitable for the secure transmission of large amounts of data in range-gated laser imaging under high noise background.Aiming at this problem,a range-gated laser imaging i...The existing image encryption schemes are not suitable for the secure transmission of large amounts of data in range-gated laser imaging under high noise background.Aiming at this problem,a range-gated laser imaging image compression and encryption method based on bidirectional diffusion is proposed.The image data collected from the range-gated laser imaging source is sparsely represented by the discrete wavelet transform.Arnold chaotic system is used to scramble the sparse matrix,and then the measurement matrix is constructed by the quantum cellular neural network(QCNN)to compress the image.In addition,the random sequence generated by QCNN hyperchaotic system is used to carry out"bidirectional diffusion"operation on the compression result,so as to realize the security encryption of image data.The comparative analysis of the security encryption performance of different compression ratios shows that the histogram sample standard of the encrypted image can reach about 10,and the information entropy value is more than 7.99,which indicates that the encryption scheme effectively hides the plaintext information of the original image.When the encrypted image is attacked by different degrees of noise,this method can still reconstruct the image through the effective decryption process.The experimental results show that this method realizes the secure compression and encryption of gated-laser imaging image data,and effectively ensures the security of data while reducing the amount of channel transmission data.展开更多
基金Project supported by the Natural Science Foundation of Jilin Province of China(Grant No.20210101417JC).
文摘Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic protocols.However,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical networks.In this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet distribution.Firstly,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot scheduling.Secondly,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key resources.Finally,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key resources.Simulation results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.
基金This research was supported in part by the National Natural Science Foundation of China under grant numbers 61672206,61572170.
文摘Network security situation awareness is an important foundation for network security management,which presents the target system security status by analyzing existing or potential cyber threats in the target system.In network offense and defense,the network security state of the target system will be affected by both offensive and defensive strategies.According to this feature,this paper proposes a network security situation awareness method using stochastic game in cloud computing environment,uses the utility of both sides of the game to quantify the network security situation value.This method analyzes the nodes based on the network security state of the target virtual machine and uses the virtual machine introspection mechanism to obtain the impact of network attacks on the target virtual machine,then dynamically evaluates the network security situation of the cloud environment based on the game process of both attack and defense.In attack prediction,cyber threat intelligence is used as an important basis for potential threat analysis.Cyber threat intelligence that is applicable to the current security state is screened through the system hierarchy fuzzy optimization method,and the potential threat of the target system is analyzed using the cyber threat intelligence obtained through screening.If there is no applicable cyber threat intelligence,using the Nash equilibrium to make predictions for the attack behavior.The experimental results show that the network security situation awareness method proposed in this paper can accurately reflect the changes in the network security situation and make predictions on the attack behavior.
基金the National Natural Science Foundation of China(Grant Nos.62272478,62202496,61872384).
文摘Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.
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The authors are highly thankful to the National Social Science Foundation of China(20BXW101,18XXW015)Innovation Research Project for the Cultivation of High-Level Scientific and Technological Talents(Top-Notch Talents of theDiscipline)(ZZKY2022303)+3 种基金National Natural Science Foundation of China(Nos.62102451,62202496)Basic Frontier Innovation Project of Engineering University of People’s Armed Police(WJX202316)This work is also supported by National Natural Science Foundation of China(No.62172436)Engineering University of PAP’s Funding for Scientific Research Innovation Team,Engineering University of PAP’s Funding for Basic Scientific Research,and Engineering University of PAP’s Funding for Education and Teaching.Natural Science Foundation of Shaanxi Province(No.2023-JCYB-584).
文摘With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.
基金This research was supported in part by the Nature Science Foundation of China(Nos.62262033,61962029,61762055,62062045 and 62362042)the Jiangxi Provincial Natural Science Foundation of China(Nos.20224BAB202012,20202ACBL202005 and 20202BAB212006)+3 种基金the Science and Technology Research Project of Jiangxi Education Department(Nos.GJJ211815,GJJ2201914 and GJJ201832)the Hubei Natural Science Foundation Innovation and Development Joint Fund Project(No.2022CFD101)Xiangyang High-Tech Key Science and Technology Plan Project(No.2022ABH006848)Hubei Superior and Distinctive Discipline Group of“New Energy Vehicle and Smart Transportation”,the Project of Zhejiang Institute of Mechanical&Electrical Engineering,and the Jiangxi Provincial Social Science Foundation of China(No.23GL52D).
文摘In a cloud environment,outsourced graph data is widely used in companies,enterprises,medical institutions,and so on.Data owners and users can save costs and improve efficiency by storing large amounts of graph data on cloud servers.Servers on cloud platforms usually have some subjective or objective attacks,which make the outsourced graph data in an insecure state.The issue of privacy data protection has become an important obstacle to data sharing and usage.How to query outsourcing graph data safely and effectively has become the focus of research.Adjacency query is a basic and frequently used operation in graph,and it will effectively promote the query range and query ability if multi-keyword fuzzy search can be supported at the same time.This work proposes to protect the privacy information of outsourcing graph data by encryption,mainly studies the problem of multi-keyword fuzzy adjacency query,and puts forward a solution.In our scheme,we use the Bloom filter and encryption mechanism to build a secure index and query token,and adjacency queries are implemented through indexes and query tokens on the cloud server.Our proposed scheme is proved by formal analysis,and the performance and effectiveness of the scheme are illustrated by experimental analysis.The research results of this work will provide solid theoretical and technical support for the further popularization and application of encrypted graph data processing technology.
文摘A lightweight malware detection and family classification system for the Internet of Things (IoT) was designed to solve the difficulty of deploying defense models caused by the limited computing and storage resources of IoT devices. By training complex models with IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Additionally, the multi-teacher knowledge distillation method is employed to train KD-LMDNet, which focuses on classifying malware families. The results indicate that the model’s identification speed surpasses that of traditional methods by 23.68%. Moreover, the accuracy achieved on the Malimg dataset for family classification is an impressive 99.07%. Furthermore, with a model size of only 0.45M, it appears to be well-suited for the IoT environment. By training complex models using IoT software gray-scale images and utilizing the gradient-weighted class-activated mapping technique, the system can identify key codes that influence model decisions. This allows for the reconstruction of gray-scale images to train a lightweight model called LMDNet for malware detection. Thus, the presented approach can address the challenges associated with malware detection and family classification in IoT devices.
基金the National Natural Science Foundation of China(61971235,61871412,and 61771258)the Six Talented Eminence Foundation of Jiangsu Province(XYDXXJS-044)+4 种基金the China Postdoctoral Science Foundation(2018M630590)the 333 High-level Talents Training Project of Jiangsu Province,the 1311 Talents Plan of Nanjing University of Posts and Telecommunications(NUPT)the Open Research Fund of Jiangsu Engineering Research Center of Communication and Network Technology,NUPT(JSGCZX17011)the Scientific Research Foundation of NUPT(NY218058)the Open Research Fund of Anhui Provincial Key Laboratory of Network and Information Security(AHNIS2020001).
文摘As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable services.However,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy threats.To address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named DA-SADA.Specifically,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart grids.Subsequently,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous authentication.In particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data aggregation.Finally,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.
基金the National Natural Science Foundation of China(No.61572521,U1636114)National Key Project of Research and Development Plan(2017YFB0802000)+2 种基金Natural Science Foundation of Shaanxi Province(2021JM-252)Innovative Research Team Project of Engineering University of APF(KYTD201805)Fundamental Research Project of Engineering University of PAP(WJY201910).
文摘Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography.Normally,the traditional way in designing a trapdoor is to identify a computationally hard problem,such as the NPC problems.So the trapdoor in a public key encryption mechanism turns out to be a type of limited resource.In this paper,we generalize the methodology of adversarial learning model in artificial intelligence and introduce a novel way to conveniently obtain sub-optimal and computationally hard trapdoors based on the automatic information theoretic search technique.The basic routine is constructing a generative architecture to search and discover a probabilistic reversible generator which can correctly encoding and decoding any input messages.The architecture includes a trapdoor generator built on a variational autoencoder(VAE)responsible for searching the appropriate trapdoors satisfying a maximum of entropy,a random message generator yielding random noise,and a dynamic classifier taking the results of the two generator.The evaluation of our construction shows the architecture satisfying basic indistinguishability of outputs under chosen-plaintext attack model(CPA)and high efficiency in generating cheap trapdoors.
基金This research was funded by the National Natural Science Foundation of China(No.U21A20451)the Science and Technology Planning Project of Jilin Province(No.20200401105GX)the China University Industry University Research Innovation Fund(No.2021FNA01003).
文摘In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.
基金supported by National Natural Science Foundation of China (Grant Nos.62102452,62172436)Natural Science Foundation of Shaanxi Province (No.2023-JCYB-584)+1 种基金Innovative Research Team in Engineering University of PAP (KYTD201805)Engineering University of PAP’s Funding for Key Researcher (No.KYGG202011).
文摘Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private data.Data aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its availability.Recently,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et al.Their scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding subset.In this paper,firstly,an efficient attack with both theorems proving and experimentative verification is launched.We find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the SM.Secondly,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption process.In addition,fault tolerance is very important to aggregation schemes in practical scenarios.In most of the current schemes,once some SMs failed,then they will not work.As far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault tolerance.Finally,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their ciphertexts.Experiments show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.
基金Project supported by the National Natural Science Foundation of China(No.U21A20451)。
文摘Information-centric satellite networks play a crucial role in remote sensing applications,particularly in the transmission of remote sensing images.However,the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands.Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content.In this paper,we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks,specifically focusing on the transmission of remote sensing images.Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time,effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion.We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction.To address these challenges,we leverage federated reinforcement learning techniques.Additionally,we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images.Through software-based simulations using a low Earth orbit satellite constellation,we validate the effectiveness of our proposed strategy,achieving a significant 17%reduction in the average delivery delay.This paper offers valuable insights into efficient content delivery in satellite networks,specifically targeting the transmission of remote sensing images,and presents a promising approach to mitigate burst traffic challenges in information-centric environments.
基金supported by the National Natural Science Foundation of China,with Fund Number 62272478.
文摘This paper presents a novel watermarking scheme designed to address the copyright protection challenges encountered with Neural radiation field(NeRF)models.We employ an embedding network to integrate the watermark into the images within the training set.Then,theNeRFmodel is utilized for 3Dmodeling.For copyright verification,a secret image is generated by inputting a confidential viewpoint into NeRF.On this basis,design an extraction network to extract embedded watermark images fromconfidential viewpoints.In the event of suspicion regarding the unauthorized usage of NeRF in a black-box scenario,the verifier can extract the watermark from the confidential viewpoint to authenticate the model’s copyright.The experimental results demonstrate not only the production of visually appealing watermarks but also robust resistance against various types of noise attacks,thereby substantiating the effectiveness of our approach in safeguarding NeRF.
基金supported by the National Natural Science Foundation of China(No.U21A20451)the Science and Technology Planning Project of Jilin Province,China(No.20220101143JC)the China University Industry-Academia-Research Innovation Fund(No.2021FNA01003)。
文摘With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates,satellites have become an important part of data transmission in air-ground networks.However,due to the factors such as geographical location and people’s living habits,the differences in user’demand for multimedia data will result in unbalanced network traffic,which may lead to network congestion and affect data transmission.In addition,in traditional satellite network transmission,the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner,which is not conducive to calculating optimal routes.The service quality requirements cannot be satisfied when multiple service requests are made.Based on the above,in this paper artificial intelligence technology is applied to the satellite network,and a software-defined network is used to obtain the global network information,perceive network traffic,develop comprehensive decisions online through reinforcement learning,and update the optimal routing strategy in real time.Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability.Compared with traditional routing,the throughput is 8%higher,and the proposed method has load balancing characteristics.
基金This work was supported by National Natural Science Foundation of China （Grant No. 61173036） and the Fundamental Research Funds for Chinese Central Universities.
文摘Recently, security in embedded system arises attentions because of modern electronic devices need cau- tiously either exchange or communicate with the sensitive data. Although security is classical research topic in world- wide communication, the researchers still face the problems of how to deal with these resource constraint devices and en- hance the features of assurance and certification. Therefore, some computations of cryptographic algorithms are built on hardware platforms, such as field program gate arrays （FPGAs）. The commonly used cryptographic algorithms for digital signature algorithm （DSA） are rivest-shamir-adleman （RSA） and elliptic curve cryptosystems （ECC） which based on the presumed difficulty of factoring large integers and the algebraic structure of elliptic curves over finite fields. Usu- ally, RSA is computed over GF（p）, and ECC is computed over GF（p） or GF（2P）. Moreover, embedded applications need advance encryption standard （AES） algorithms to pro- cess encryption and decryption procedures. In order to reuse the hardware resources and meet the trade-off between area and performance, we proposed a new triple functional arith- metic unit for computing high radix RSA and ECC operations over GF（p） and GF（2P）, which also can be extended to support AES operations. A new high radix signed digital （SD） adder has been proposed to eliminate the carry propagations over GF（p）. The proposed unified design took up 28.7% less hardware resources than implementing RSA, ECC, and AES individually, and the experimental results show that our proposed architecture can achieve 141.8 MHz using approxi- mately 5.5k CLBs on Virtex-5 FPGA.
基金This work was supported by the National Natural Science Foundation of China(Grant Nos.61872384 and U1636114)partly supported by the Natural Science Foundation of Engineering University of PAP(Grant No.WJY201915).
文摘H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography.In this paper,a novel motion vector(MV)based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion.Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm.In the embedding block,the motion vector space encoding is used to embed a(2n+1)-ary notational number into an n-dimension vector composed of motion vectors generated from the selection block.Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding.The re-motion compensation(re-MC)block will re-construct the macroblock(MB)whose motion vectors have been modified by embedding block.System block plays the role of the generator for chaotic sequences and encryptor for secret data.Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion,it also presents good undetectability for existing MV-based steganalysis feature.Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.
基金Projects(61071096,61003233,61073103)supported by the National Natural Science Foundation of ChinaProjects(20100162110012,20110162110042)supported by the Research Fund for the Doctoral Program of Higher Education of China
文摘Mission planning was thoroughly studied in the areas of multiple intelligent agent systems,such as multiple unmanned air vehicles,and multiple processor systems.However,it still faces challenges due to the system complexity,the execution order constraints,and the dynamic environment uncertainty.To address it,a coordinated dynamic mission planning scheme is proposed utilizing the method of the weighted AND/OR tree and the AOE-Network.In the scheme,the mission is decomposed into a time-constraint weighted AND/OR tree,which is converted into an AOE-Network for mission planning.Then,a dynamic planning algorithm is designed which uses task subcontracting and dynamic re-decomposition to coordinate conflicts.The scheme can reduce the task complexity and its execution time by implementing real-time dynamic re-planning.The simulation proves the effectiveness of this approach.
基金supported by the National Key Research and Development Projects(No.2018YFB1800303)the Natural Science Foundation of Jilin Province(No.20190201188JC)the Research on Teaching Reform of Higher Education in Jilin Province(No.JLLG685520190725093004)
文摘Aiming at the security problem of range gated laser imaging in high noise background,a range gated laser image encryption scheme based on the quantum genetic algorithm(QGA)is proposed.Due to the fuzziness of the laser image itself,the randomness and security of the key become more and more important in encryption.In this paper,the chaotic sequence is used as the parent chromosome of the QGA,and the random number satisfying the encryption algorithm is obtained by an iterative genetic algorithm.To further improve the security of laser images,some random pixels are stochastically inserted around the laser image before scrambling.These random pixels are scrambled together with the image.Finally,an adaptive diffusion method is designed to completely change the original statistical information of the image.Experimental simulation and performance analysis show that the scheme has high security.
基金Project supported by the National Natural Science Foundation of China(Nos.U1636114,61572521,and 61772550)the Innovative Research Team in Engineering University of People’s Armed Police,China(No.KYTD201805)+1 种基金the Natural Science Foundation of Shaanxi Province,China(No.2021JM-252)the Basic Research Project of Engineering University of People’s Armed Police,China(No.WJY201914)。
文摘Threshold proxy re-encryption(TPRE)can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies,and can also provide normal services even when certain proxy servers are paralyzed or damaged.A non-interactive identity-based TPRE(IB-TPRE)scheme over lattices is proposed which removes the public key certificates.To accomplish this scheme,Shamir’s secret sharing is employed twice,which not only effectively hides the delegator’s private key information,but also decentralizes the proxy power by splitting the re-encryption key.Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share.This property is achieved by lattice-based fully homomorphic signatures.As a result,the whole scheme is thoroughly capable of resisting quantum attacks even when they are available.The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model.Two typical application scenarios,including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography,are presented.
基金supported by the National Key Research and Development Projects(No.2018YFB1800303)the Natural Science Foundation of Jilin Province(No.20190201188JC)the Research on Teaching Reform of Higher Education in Jilin Province(No.JLLG685520190725093004)。
文摘The existing image encryption schemes are not suitable for the secure transmission of large amounts of data in range-gated laser imaging under high noise background.Aiming at this problem,a range-gated laser imaging image compression and encryption method based on bidirectional diffusion is proposed.The image data collected from the range-gated laser imaging source is sparsely represented by the discrete wavelet transform.Arnold chaotic system is used to scramble the sparse matrix,and then the measurement matrix is constructed by the quantum cellular neural network(QCNN)to compress the image.In addition,the random sequence generated by QCNN hyperchaotic system is used to carry out"bidirectional diffusion"operation on the compression result,so as to realize the security encryption of image data.The comparative analysis of the security encryption performance of different compression ratios shows that the histogram sample standard of the encrypted image can reach about 10,and the information entropy value is more than 7.99,which indicates that the encryption scheme effectively hides the plaintext information of the original image.When the encrypted image is attacked by different degrees of noise,this method can still reconstruct the image through the effective decryption process.The experimental results show that this method realizes the secure compression and encryption of gated-laser imaging image data,and effectively ensures the security of data while reducing the amount of channel transmission data.
