How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of th...In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.展开更多
二进制漏洞检测在程序安全领域有着重要的作用,为应对大规模的漏洞检测任务,越来越多的神经网络技术被应用到跨架构漏洞检测中,这些技术显著提高了漏洞检测的准确率,但是现有方法仍然面临提取到的信息单一、不能进行跨架构漏洞检测等问...二进制漏洞检测在程序安全领域有着重要的作用,为应对大规模的漏洞检测任务,越来越多的神经网络技术被应用到跨架构漏洞检测中,这些技术显著提高了漏洞检测的准确率,但是现有方法仍然面临提取到的信息单一、不能进行跨架构漏洞检测等问题。提出了一种融合语义与属性特征的跨架构漏洞检测方法。使用二进制函数的汇编代码和属性控制流图作为输入,提取基本块中所有汇编代码的语义信息,将基本块级的语义信息与属性特征信息进行特征融合,生成139维的基本块级向量表示,以此来更全面地表示函数的语义和属性信息。使用基于卷积神经网络的孪生网络模型生成函数级的嵌入向量,以此来提取不同基本块中不同空间层次结构的特征并减少神经网络的参数量,通过计算函数级嵌入向量的距离来判断待检测的两个二进制函数是否相似。在进行跨架构漏洞检测时,只需要输入二进制文件中的函数和已知漏洞函数的汇编代码和属性控制流图即可完成漏洞检测。实验结果表明,该方法检测的准确率为95.64%,AUC(area under curve)值为0.9969,与现有方法相比,准确率可以提升0.26~7.04个百分点,AUC可以提升0.11~1.59个百分点,在真实环境的漏洞检测中表现优异。展开更多
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘In this paper,a novel method of ultra-lightweight convolution neural network(CNN)design based on neural architecture search(NAS)and knowledge distillation(KD)is proposed.It can realize the automatic construction of the space target inverse synthetic aperture radar(ISAR)image recognition model with ultra-lightweight and high accuracy.This method introduces the NAS method into the radar image recognition for the first time,which solves the time-consuming and labor-consuming problems in the artificial design of the space target ISAR image automatic recognition model(STIIARM).On this basis,the NAS model’s knowledge is transferred to the student model with lower computational complexity by the flow of the solution procedure(FSP)distillation method.Thus,the decline of recognition accuracy caused by the direct compression of model structural parameters can be effectively avoided,and the ultralightweight STIIARM can be obtained.In the method,the Inverted Linear Bottleneck(ILB)and Inverted Residual Block(IRB)are firstly taken as each block’s basic structure in CNN.And the expansion ratio,output filter size,number of IRBs,and convolution kernel size are set as the search parameters to construct a hierarchical decomposition search space.Then,the recognition accuracy and computational complexity are taken as the objective function and constraint conditions,respectively,and the global optimization model of the CNN architecture search is established.Next,the simulated annealing(SA)algorithm is used as the search strategy to search out the lightweight and high accuracy STIIARM directly.After that,based on the three principles of similar block structure,the same corresponding channel number,and the minimum computational complexity,the more lightweight student model is designed,and the FSP matrix pairing between the NAS model and student model is completed.Finally,by minimizing the loss between the FSP matrix pairs of the NAS model and student model,the student model’s weight adjustment is completed.Thus the ultra-lightweight and high accuracy STIIARM is obtained.The proposed method’s effectiveness is verified by the simulation experiments on the ISAR image dataset of five types of space targets.
文摘二进制漏洞检测在程序安全领域有着重要的作用,为应对大规模的漏洞检测任务,越来越多的神经网络技术被应用到跨架构漏洞检测中,这些技术显著提高了漏洞检测的准确率,但是现有方法仍然面临提取到的信息单一、不能进行跨架构漏洞检测等问题。提出了一种融合语义与属性特征的跨架构漏洞检测方法。使用二进制函数的汇编代码和属性控制流图作为输入,提取基本块中所有汇编代码的语义信息,将基本块级的语义信息与属性特征信息进行特征融合,生成139维的基本块级向量表示,以此来更全面地表示函数的语义和属性信息。使用基于卷积神经网络的孪生网络模型生成函数级的嵌入向量,以此来提取不同基本块中不同空间层次结构的特征并减少神经网络的参数量,通过计算函数级嵌入向量的距离来判断待检测的两个二进制函数是否相似。在进行跨架构漏洞检测时,只需要输入二进制文件中的函数和已知漏洞函数的汇编代码和属性控制流图即可完成漏洞检测。实验结果表明,该方法检测的准确率为95.64%,AUC(area under curve)值为0.9969,与现有方法相比,准确率可以提升0.26~7.04个百分点,AUC可以提升0.11~1.59个百分点,在真实环境的漏洞检测中表现优异。
