The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The metho...The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.展开更多
With the development of natural language processing,deep learning,and other technologies,text steganography is rapidly developing.However,adversarial attack methods have emerged that gives text steganography the abili...With the development of natural language processing,deep learning,and other technologies,text steganography is rapidly developing.However,adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis.If terrorists use the text steganography method to spread terrorist messages,it will greatly disturb social stability.Steganalysis methods,especially those for resisting adversarial attacks,need to be further improved.In this paper,we propose a two-stage highly robust model for text steganalysis.The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels.In the first phase,every sentence is first transformed into word vectors.To obtain a high dimensional sentence vector,we use Bi-LSTM to obtain feature information for all words in the sentence while retaining strong correlations.In the second phase,we input multiple sentences vectors into the GNN,from which we extract inter-sentential anomaly features and make a judgment as to whether the text contains secret messages.In addition,to improve the robustness of the model,we add adversarial examples to the training set to improve the robustness and generalization of the steganalysis model.Theoretically,our proposed method is more robust and more accurate in detection compared to existing methods.展开更多
基金This project is supported by the National Natural Science Foundation of China(Grant No.51305211)Natural Science Foundation of Jiangsu(Grant No.BK20160955)Jiangsu Government Scholarship for Overseas Studies,College students practice and innovation training project of Jiangsu province(Grant No.201710300218),and the PAPD。
文摘The signal processing problem has become increasingly complex and demand high acquisition system,this paper proposes a new method to reconstruct the structure phased array structural health monitoring signal.The method is derived from the compressive sensing theory and the signal is reconstructed by using the basis pursuit algorithm to process the ultrasonic phased array signals.According to the principles of the compressive sensing and signal processing method,non-sparse ultrasonic signals are converted to sparse signals by using sparse transform.The sparse coefficients are obtained by sparse decomposition of the original signal,and then the observation matrix is constructed according to the corresponding sparse coefficients.Finally,the original signal is reconstructed by using basis pursuit algorithm,and error analysis is carried on.Experimental research analysis shows that the signal reconstruction method can reduce the signal complexity and required the space efficiently.
基金This work is supported by the National Natural Science Foundation of China under Grant U1836110,U1836208by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20200039+3 种基金by China Postdoctoral Science Foundation(2017M610574)by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,Chinaby the Opening Project of Guangxi Key Laboratory of Cryptography and Information Security(No.GCIS201713)Guangdong Provincial Key Laboratory of Data Security and Privacy Protection(Grant No.2017B03031004)。
文摘With the development of natural language processing,deep learning,and other technologies,text steganography is rapidly developing.However,adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis.If terrorists use the text steganography method to spread terrorist messages,it will greatly disturb social stability.Steganalysis methods,especially those for resisting adversarial attacks,need to be further improved.In this paper,we propose a two-stage highly robust model for text steganalysis.The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels.In the first phase,every sentence is first transformed into word vectors.To obtain a high dimensional sentence vector,we use Bi-LSTM to obtain feature information for all words in the sentence while retaining strong correlations.In the second phase,we input multiple sentences vectors into the GNN,from which we extract inter-sentential anomaly features and make a judgment as to whether the text contains secret messages.In addition,to improve the robustness of the model,we add adversarial examples to the training set to improve the robustness and generalization of the steganalysis model.Theoretically,our proposed method is more robust and more accurate in detection compared to existing methods.
