The existing image tamper detection networks based on deep learning often have problems such as low detection accuracy and weak algorithm transferability. To address the above issues， a two-channel progressive feature filtering network was proposed. Two channels were used to extract the two-domain features of the image in parallel， one of which was used to extract the shallow and deep features of the image spatial domain， and the other channel was used to extract the feature distribution of the image noise domain. At the same time， a progressive subtle feature screening mechanism was used to filter redundant features and gradually locate the tampered regions； in order to extract the tamper mask more accurately， a two-channel subtle feature extraction module was proposed， which combined the subtle features of the spatial domain and the noise domain to generate a more accurate tamper mask. During the decoding process， the localization ability of the network to tampered regions was improved by fusing filtered features of different scales and the contextual information of the network. The experimental results show that in terms of detection and localization， compared with the existing advanced tamper detection networks ObjectFormer， Multi-View multi-Scale Supervision Network （MVSS-Net） and Progressive Spatio-Channel Correlation Network （PSCC-Net）， the F1 score of the proposed network is increased by an 10.4， 5.9 and 12.9 percentage points on CASIA V2.0 dataset； when faced with Gaussian low-pass filtering， Gaussian noise， and JPEG compression attacks， compared with Manipulation Tracing Network （ManTra-Net） and Spatial Pyramid Attention Network （SPAN）， the Area Under Curve （AUC） of the proposed network is increased by 10.0 and 5.4 percentage points at least. It is verified that the proposed network can effectively solve the problems of low detection accuracy and poor transferability in the tamper detection algorithm.

Aiming at the copyright protection problem of 3D medical images and the simultaneous expansion problem of watermark storage capacity caused by the increase of the number of images to be protected， a robust zero-watermarking algorithm based on ray-casting sampling and polar complex exponential moment was proposed. Firstly， a sampling algorithm based on ray-casting was proposed to sample the features of 3D medical images composed of multiple sequences of 2D medical images and describe these features in 2D image space. Secondly， a robust zero-watermarking algorithm for 3D medical images was proposed. In the algorithm， three 2D feature images of coronal， sagittal planes and cross section of the 3D medical image were obtained by ray-casting sampling， and the three 2D feature images were transformed by polar complex exponential to obtain the quaternion orthogonal moment. Finally， the zero-watermarking information was constructed by using the quadratic orthogonal moment and Logistic chaotic encryption. Simulation results show that the proposed algorithm can maintain the bit correctness rate of zero-watermarking extraction above 0.920 0 under various common image processing attacks and geometric attacks； the watermark storage capacity of the proposed algorithm can be improved with the increase of the volume of 3D medical image data， and the storage capacity of the proposed algorithm has been improved by 93.75% at least compared to the other 2D medical image zero-watermarking algorithms for comparison.

The accuracy of Inverse Distance Weighting （IDW） will be affected by the selection of reference points and parameters. Aiming at the problem of ignoring local characteristics in multi-Parameter co-optimization Inverse Distance Weighting algorithm （PIDW）， an improved algorithm based on particle swarm local optimized IDW was proposed， namely Particle swarm Local optimization Inverse Distance Weight （PLIDW）. Firstly， the parameters of each sample point in the study area were optimized respectively， and the cross-validation method was used for evaluation， and the optimal set of parameters for each sample point was recorded. At the same time， in order to improve the query efficiency， a K-Dimensional Tree （KD-Tree） was used to save the spatial positions and optimal parameters. Finally， according to the spatial proximity， the nearest set of parameters was selected from KD-Tree to optimize IDW. Experimental results based on simulated data and real temperature dataset show that compared with PIDW， PLIDW has the accuracy on the real dataset improved by more than 4.18%. This shows that the low accuracy in some scenarios caused by ignoring local features in PIDW is improved by the proposed algorithm， and the adaptability is increased at the same time.

There are limited memory and computing power of the equipment in smart construction sites， making it very difficult to detect rebar in real time through object detection on the on-site equipment. The slow speed of rebar detection and the high cost of model deployment of this equipment also bring great challenges. In order to solve the problems， RebarNet， a lightweight network for rebar detection with attention mechanism was proposed on the basis of YOLOv3 （You Only Look Once version 3）. Firstly， the residual block was used as the basic unit of the network to construct a feature extraction structure to extract local and contextual information. Secondly， Channel Attention （CA） module and Spatial Attention （SA） module were added to the residual block to adjust the attention weight of the feature map and improve the ability of the network to extract features. Thirdly， the feature pyramid fusion module was used to increase the receptive field of the network and optimize the extraction effect of the medium-sized rebar images. Finally， the feature map of 52×52 channel was output for post-processing and rebar detection after 8 times downsampling. Experimental results show that the parameter amount of the proposed network is only 5% of that of Darknet53 network， and mAP （mean Average Precision） of the proposed network achieves 92.7% at the speed of 106.8 FPS （Frames Per Second） on the rebar test dataset. Compared with the existing 8 object detection networks including EfficientDet （Scalable and Efficient Object Detection）， SSD （Single Shot MultiBox Detector）， CenterNet， RetinaNet， Faster RCNN （Faster Region-CNN）， YOLOv3， YOLOv4 and YOLOv5m （YOLOv5 medium）， RebarNet has a shorter training time （24.5 seconds）， the lowest memory usage （1 956 MB）， and the smallest model weight file （13 MB）. Compared with the current best-performing YOLOv5m network， RebarNet has the mAP slightly lower by 0.4 percentage points with the detection speed increased by 48 FPS， which is 1.8 times of that of YOLOv5m network. The above indicates that the proposed network helps to complete the task of high-efficiency and accurate rebar detection in smart construction sites.

Prediction results of transaction pricing of transport service orders in internet freight transport platform are the direct reflections of both platform operation strategy and carrier decision， and influences both platform benefits and the healthy development of carrier market significantly. Taking internet freight transport platform of SF Express network as an example， the data were preprocessed through missing value processing and categorical data conversion. Aiming at the prediction precision problem of transaction pricing in internet freight transport platform， a new prediction model of transaction pricing in internet freight transport platform based on combination of dual Long Short-Term Memory networks（LSTM） was designed， and the prediction results were analyzed by K-means clustering. Compared with the models such as LSTM， Support Vector Regression （SVR）， Long Short-Term-Memory combined with Support Vector Regression （LSTM-SVR）， and combination of grey GM（1，1） and Back Propagation （BP） （GM（1，1）-BP）， the combination model of dual LSTM has the lowest Mean Absolute Error （MAE）， Mean Square Error （MSE）， Mean Absolute Percentage Error （MAPE） and highest R square （R²）， which is 9.90， 402.54， 1.48 and 0.999 97 respectively. The evaluation results of predicted order transaction pricing by using K-means clustering analysis are consistent with the actual values. Experimental results indicate that， the proposed combination model of dual LSTM has effectiveness and precise prediction effect of transaction pricing in internet freight transport platform.

Knowledge Graph （KG） can effectively extract information by efficiently organizing massive data. Therefore， recommendation methods based on knowledge graph have been widely studied and applied. Aiming at the sampling error problem of graph neural network in knowledge graph modeling， a method of Non-sampling Collaborative Knowledge graph Network （NCKN） was proposed. Firstly， a non-sampling knowledge dissemination module was designed， in which linear aggregators with different sizes were used in a single convolutional layer to capture deep-level information and achieve efficient non-sampling pre-computation. Then， in order to distinguish the contribution degrees of neighbor nodes， attention mechanism was introduced in the dissemination process. Finally， the collaboration signal of user interaction and knowledge embedding were combined in the collaborative dissemination module to better describe user preferences. Based on three real datasets， the performance of NCKN in CTR （Click Through Rate） prediction and Top-k was evaluated. The experimental results show that compared with the mainstream algorithms RippleNet （Ripple Network） and KGCN （Knowledge Graph Convolutional Network）， the accuracy of NCKN in CTR prediction increases by 2.71% and 4.60%， respectively； in the Top-k forecast， prediction， the accuracy of NCKN increases by 5.26% and 3.91% on average respectively. The proposed method not only solves the sampling error problem of graph neural network in knowledge map modeling， but also improves the accuracy of the recommended model.

Considering the seasonal， nonlinear and non-stationary characteristics of air passenger demand series， an air passenger demand forecasting model based on a dual decomposition and reconstruction strategy was proposed. Firstly， the air passenger demand series was decomposed twice by Seasonal and Trend decomposition using Loess （STL） and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise （CEEMDAN） methods， and the components were reconstructed based on the feature analysis results of complexity and correlation. Then， Seasonal AutoRegressive Integrated Moving Average （SARIMA）， AutoRegressive Integrated Moving Average （ARIMA）， Kernel based Extreme Learning Machine （KELM） and Bidirectional Long Short-Term Memory （BiLSTM） network models were selected by model matching strategy to predict each reconstructed component respectively， among which the hyperparameters of KELM and BiLSTM models were determined by the Adaptive Tree of Parzen Estimators （ATPE） algorithm. Finally， the prediction results of the reconstruction components were linearly integrated. The air passenger demand data collected from Beijing Capital International Airport， Shenzhen Bao’an International Airport and Haikou Meilan International Airport were taken as research subjects for one-step and multi-step ahead prediction experiments. Experimental results show that compared with the single decomposition ensemble model STL-SAAB， the proposed model has the Root Mean Square Error （RMSE） improved by 14.98% to 60.72%. It can be seen that guided by the idea of “divide and rule”， the proposed model combines model matching and reconstruction strategies to extract the inherent development pattern of the data， which provides a new thinking to scientifically predict the change of air passenger demand.

Aiming at the problem of multiple types of application?layer Distributed Denial of Service （DDoS） attacks， which are difficult to detect simultaneously， an application?layer DDoS attack detection method based on integrated learning was proposed to detect multiple types of application?layer DDoS attacks. Firstly， by using the dataset generation module， the normal and attack traffic was simulated， the corresponding feature information was filtered and extracted， and 47?dimensional feature information characterized Challenge Collapsar （CC）， HTTP Flood， HTTP Post and HTTP Get attacks were generated. Secondly， by using the offline training module， the effective features were processed and input into the integrated Stacking detection model for training， thereby obtaining a detection model that can detect multiple types of application?layer DDoS attacks. Finally， by using the online detection module， the specific traffic type of the traffic to be detected was judged through deploying the detection model online. Experimental results show that compared with the classification models constructed by Bagging，Adaboost and XGBoost，the Stacking integretion model improves the accuracy by 0. 18 percentage points，0. 21 percentage points and 0. 19 percentage points respectively，and has the malicious traffic detection rate reached 98% under the optimal time window. It can be seen that the proposed method has good performance in detecting multi-type application-layer DDoS attacks.

When big data flow calculation tasks with different attributes generated by networked vehicle nodes are transmitted and offloaded， issues such as time delay jitter， large computational energy consumption and system overhead usually happen. Therefore， according to the actual communication environment， a scheme for task offloading and resource allocation based on Simulated Annealing Algorithm （SAA） in Cellular Vehicle to Everything （C-V2X） Internet of Vehicles （IoV） was proposed. Firstly， according to the task processing priority， the tasks with high processing priority were processed by collaborative offloading and computing. Secondly， an SAA-based task offloading strategy was developed with the aid of globally searching for the optimal offloading scale factor. And the task offloading scale factor was analyzed and optimized. Finally， during the update process of task offloading scale factor， the problem of minimizing the system overhead was transformed into the convex optimization problem of power and computational resource allocation. And the Lagrange multiplier method was used to obtain the optimal solution. By comparing the proposed algorithm with the local offloading and adaptive genetic algorithm， it can be seen that： as the calculation task data size increases， the time delay， power consumption and system overhead of the adaptive genetic algorithm are decreased by 5.97%， 49.40%， and 49.36% respectively， compared with those of the local offloading. On this basis， the time delay， power consumption and system overhead of the proposed SAA-based scheme are further decreased by 6.35%， 92.27%， and 91.7% respectively， compared with those of the adaptive genetic algorithm. As the CPU cycles of the calculation task increase， the time delay， power consumption and system overhead of the adaptive genetic algorithm are decreased by 16.4%， 49.58%， and 49.23% respectively， compared with local offloading. On this basis， the time delay， power consumption and system overhead of the proposed SAA-based scheme are further decreased by 19.61%， 94.39%， and 89.88% respectively， compared with those of the adaptive genetic algorithm. Experimental results show that SAA cannot only reduce the time delay， power consumption and system overhead of communication systems but also accelerate convergence of the results.

Aiming at the problem that existing watermarking algorithms based on deep learning cannot effectively protect the copyright of high-dimensional medical images， a medical image watermarking algorithm based on multiscale knowledge learning was proposed for the copyright protection of diffusion-weighted images. First， a watermark embedding network based on multiscale knowledge learning was proposed to embed watermarks， and the semantic， texture， edge and frequency domain information of the diffusion-weighted image was extracted by a fine-tuned pre-training network as multiscale knowledge features. Then， the multiscale knowledge features were combined to reconstruct the diffusion-weighted image， and a watermark was embedded during the process redundantly to obtain a watermarked diffusion-weighted image highly similar to the original one visually. Finally， a watermark extraction network based on pyramid feature learning was proposed to improve the robustness of the algorithm by learning the distribution correlation of watermarking signals from different scales of context in the watermarked diffusion-weighted image. Experimental results show that the average Peak Signal-to-Noise Ratio （PSNR） of the reconstructed watermarked images by the proposed algorithm reaches 57.82 dB. Since diffusion-weighted images need to meet certain diffusivity features when converting to diffusion tensor images， the proposed algorithm only has 8 pixel points with the deflection angle of the principal axis direction greater than 5°， and none of these 8 pixel points is in the region of interest of the image. Besides， both of the Fraction Anisotropy （FA） and the Mean Diffusivity （MD） of the image generated by the proposed algorithm are close to 0， which fully meets the requirements of clinical diagnosis. At the same time， facing common noise attacks such as those with cropping strength less than 0.7 and rotation angle less than 15， the proposed algorithm achieves more than 95% watermarking accuracy and can effectively protect the copyright information of diffusion-weighted images.

Flow entries are forwarding rules generated by controllers and guide switches to process data packets in Software Defined Network （SDN）. Every flow entry is stored in the memory of switches and has timeout， which affects the bandwidth cost in SDN control channel， the memory consumption in switches， and the system’s resource management and performance. As most of the existing SDN performance optimization schemes only have single objective， and do not consider the impact of the types and time of the flow entry timeouts， a multi-objective optimization scheme was proposed based on the dynamic mixed timeouts of flow entries to simultaneously optimize the three objects： the detection of elephant flows， the memory consumption of flow entries in switches， and the control channel bandwidth occupation. In the dynamic mixed timeout， hard-timeout and idle-timeout， two timeout methods of flow entries were combined， and the timeout type and time of flow entries were adjusted in a two-dimensional dynamic way. The NSGA-Ⅱ algorithm was used to solve the proposed optimization problem and to evaluate the impact of different timeout methods and timeout time on the three optimization objectives. The solution set of specific timeouts was combined with the solution set of Bayesian multi-objective optimization algorithm to improve the quality of the solution set. The results show that the proposed scheme can provide a higher detection accuracy， a lower bandwidth occupation， and a smaller switch memory consumption. It significantly improves the overall performance of SDNs.

There are various abnormal phenomena before the earthquake, and how to effectively extract exception information before the earthquake is a very important research topic. The geometric moving average martingale algorithm based on sliding window was proposed to extract the anomaly features before earthquake. The seismic data were processed by geometric moving average martingale and sliding window feature extraction, and the anomaly features of earthquake could be effectively extracted before earthquake. Through the analysis of the NOAA (National Oceanic and Atmospheric Administration satellite outgoing long wave radiation information before the Wenchuan earthquake and Lushan earthquake, the experiments show that the algorithm can detect that the earthquake area is more obviously abnormal than the surrounding area. This anomaly can help researchers determine earthquake area before the earthquake.

Concerning the flaw that it needs a fully credible third party in ID-based strong designated verifier signature and the low efficiency of existing schemes, taking the advantage of Certificate Authority (CA)'s low trust level in certificate-based public key cryptography, a new strong designated verifier signature scheme was proposed in this paper. Furthermore, the formal security analysis under assumed Bilinear Diffie-Hellman (BDH) problem in the random oracle model was presented. Performance analysis shows the scheme meets all properties of strong designated verifier signature schemes and enjoys higher correspondence efficiency used in bandwidth limited environment since the signature length is just one of the group elements.

Software security measurement is critical to the development of software and improvement of software security. Based on the entropy and attack surface proposed by Manadhata et al. (MANADHATA P K, TAN K M C, MAXION R A, et al. An approach to measuring a system's attack surface, CMU-CS-07-146. Pittsburgh: Carnegie Mellon University, 2007; MANADHATA P K, WING J M. An attack surface metric. IEEE Transactions on Software Engineering, 2011, 37(3): 371-386), a method of software security measurement was used to assess the threat of the software's resources and provide the threat weight of these resources. Based on the threat weight, the attack surface metric was calculated for determining whether a software product is secure in design, or in what aspect the software product can be improved. The method is demonstrated in a case to show that, when using the method, the probable security threats can be found as early as possible to prevent from producing the software products that may have vulnerabilities, and the directions for the improvement of software security are pointed out clearly.

To improve the computational efficiency in the threshold signature scheme, the authors proposed a new threshold signature scheme based on bilinear pairing via combining Gennaro's (GENNARO R, JAREAKI S, KRAWCZYK H, et al. Secure distributed key generation for discrete-log based cryptosystem. Journal of Cryptology, 2007, 20(1): 51-83) distributed secret key generation solution and Gu's (GU K, JIA W J, JIANG C L. Efficient and secure identity-based signature scheme. Journal of Software, 2011, 22(6): 1350-1360) signature scheme in the standard model. There was no trusted dealer for secret key share distribution and each party could verify the validity of some important information, which guaranteed the proposed scheme can avoid the malicious private key generator attack and public key share replacing attack. The comparison results with two previous threshold signature schemes show that the proposed scheme needs less pairing computation and raises the computational efficiency.

By discussing the shortages of access control policy, and analyzing the complementarity and completeness between access control and state control, the necessity of state control was proposed. A formal description about state control policy was defined, and the policy's description rules based on XML were regulated. At the same time, according to different control goal and control object, some application patterns for state control policy were provided. In addition, the complexity of state control policy was discussed, and some solutions were provided.

The blind signature is widely applied to the electronic voting system and electronic paying system, etc. While giving a blind signature, the signer does not know the content of the signed message. This paper analyzed the security of a new certificateless proxy blind signature scheme (WEI CHUN-YAN, CAI XIAO-QIU. New certificateless proxy blind signature scheme. Journal of Computer Applications, 2010,30(12):3341-3342) and found out the security loophole. The signer can link the signed message with the original message. Thus the scheme can not satisfy the security requirements of the blind signature scheme. To solve this problem, an improved scheme was proposed. The improved scheme eliminates the defect of the original one.

It is difficult to effectually track and monitor a vessel by video monitoring in narrow and winding controlled areas of Yangtze River. In this situation, directly using the common method for maritime video surveillance would result in tracking lag, alignment error, and even loss of target. A method of combining Automatic Identification System (AIS) and multiple Pan-Tilt-Zoom (PTZ) cameras was proposed to trace and monitor the vessel in controlled areas of Yangtze River. The latitude and longitude of the target vessel were utilized to initialize the alignment of PTZ cameras. Meanwhile, vessel’s speed and heading were applied for dynamic prediction tracking to smoothen image and eliminate tracking lag, while image recognition was employed to correct error for ensuring that target vessel was located in the optimum surveillance area.The simulation results indicate that this algorithm can not only track target vessel continuously and dynamically, but also realize the real-time target tracking with high precision.

The braid group is a new candidate platform for constructing quantum attack-resistant public key encryption system. The proxy signature allows a proxy signer to sign messages on behalf of an original signer who delegates his signing power to the proxy signer. A proxy signature based on root finding problem over braid group was put forward, the analysis of its correctness, security, efficiency and parameter choice were subsequently presented, and afterwards the detailed formal security proofs in the Random Oracle Model (ROM) were given. The new scheme can resist current known attacks.

In view of the background update problems based on Gaussian Mixture Model (GMM), such as sudden illumination change, mutual transformation between target and background, a new background auto-adapted update algorithm was proposed in this paper. First, the algorithm distinguished whether the sudden change of illumination occurred according to the current tested target size, and took pertinent updating measures. If there is not sudden change of illumination, the backgrouds of backgroud region and target region were updated seperately. The update of the target region was mainly discussed, and the method that adjusted the background update rate of the target region according to the targets characteristic parameters, such as size, velocity of movement and match times was proposed. The simulation results show that the algorithm not only guarantees the integrity of target detection, but also improves the adaptation of model to background changes.

In order to make a better agreement on both the discovery results of Semantic Web services and the intentions of the service requester, advance the accuracy rate of service discovery, and improve the automation performance in service discovery and selection, the context knowledge of service provider and requester was added into service description, and the planning method was employed to achieve the meaning understanding for the private ontology concepts between service participants. Experiments using the standard services test sets show that the performance of service discovery was improved significantly, though the discovery was delayed. In particular, when domain ontologies that are used to describe concerning services have many semantics difference, the result of discovering services is more precise.