Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily d...Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.展开更多
The performance evaluation of automatic carrier landing system(ACLS)is an important part in the field of carrier aircraft landing control.Combining grey analytic hierarchy theory and data normalization theory,an impro...The performance evaluation of automatic carrier landing system(ACLS)is an important part in the field of carrier aircraft landing control.Combining grey analytic hierarchy theory and data normalization theory,an improved grey analytic hierarchy method is introduced to evaluate the performance of ACLS.A complete performance evaluation indicators system of ACLS is established,and the definition and calculation formula of each indicator are provided.The grey analytic hierarchy model is modified to improve the real-time performance of the algorithm,where traditional expert scoring sampling matrix is substituted by an indicator normalized sample matrix.Taking a certain ACLS as an example,the experimental simulation is carried out,and the simulation results verify the reliability and the accuracy of the improved grey analytic hierarchy method.展开更多
基金supported in part by the National Natural Science Foundation(62202118,U24A20241)in part by Major Scientific and Technological Special Project of Guizhou Province([2024]014,[2024]003)+1 种基金in part by Scientific and Technological Research Projects from Guizhou Education Department(Qian jiao ji[2023]003)in part by Guizhou Science and Technology Department Hundred Level Innovative Talents Project(GCC[2023]018).
文摘Deep neural networks remain susceptible to adversarial examples,where the goal of an adversarial attack is to introduce small perturbations to the original examples in order to confuse the model without being easily detected.Although many adversarial attack methods produce adversarial examples that have achieved great results in the whitebox setting,they exhibit low transferability in the black-box setting.In order to improve the transferability along the baseline of the gradient-based attack technique,we present a novel Stochastic Gradient Accumulation Momentum Iterative Attack(SAMI-FGSM)in this study.In particular,during each iteration,the gradient information is calculated using a normal sampling approach that randomly samples around the sample points,with the highest probability of capturing adversarial features.Meanwhile,the accumulated information of the sampled gradient from the previous iteration is further considered to modify the current updated gradient,and the original gradient attack direction is changed to ensure that the updated gradient direction is more stable.Comprehensive experiments conducted on the ImageNet dataset show that our method outperforms existing state-of-the-art gradient-based attack techniques,achieving an average improvement of 10.2%in transferability.
文摘The performance evaluation of automatic carrier landing system(ACLS)is an important part in the field of carrier aircraft landing control.Combining grey analytic hierarchy theory and data normalization theory,an improved grey analytic hierarchy method is introduced to evaluate the performance of ACLS.A complete performance evaluation indicators system of ACLS is established,and the definition and calculation formula of each indicator are provided.The grey analytic hierarchy model is modified to improve the real-time performance of the algorithm,where traditional expert scoring sampling matrix is substituted by an indicator normalized sample matrix.Taking a certain ACLS as an example,the experimental simulation is carried out,and the simulation results verify the reliability and the accuracy of the improved grey analytic hierarchy method.
