Adversarial attack for time-series classification model is widely explored and many attack methods are proposed.But there is not a method of attack based on the data itself.In this paper,we innovatively proposed a bla...Adversarial attack for time-series classification model is widely explored and many attack methods are proposed.But there is not a method of attack based on the data itself.In this paper,we innovatively proposed a black-box sparse attack method based on data location.Our method directly attack the sensitive points in the time-series data accord-ing to statistical features extract from the dataset.At frst,we have validated the transferability of sensitive points among DNNs with different structures.Secondly,we use the statistical features extract from the dataset and the sensi-tive rate of each point as the training set to train the predictive model.Then,predicting the sensitive rate of test set by predictive model.Finally,perturbing according to the sensitive rate.The attack is limited by constraining the LO norm to achieve one-point attack.We conduct experiments on several datasets to validate the effectiveness of this method.展开更多
基金This work is supported by the Key Program of National Natural Science Foundation of China(No.61832004)International Cooperation and Exchange Program of National Natural Science Foundation of China(Grant no.62061136006).
文摘Adversarial attack for time-series classification model is widely explored and many attack methods are proposed.But there is not a method of attack based on the data itself.In this paper,we innovatively proposed a black-box sparse attack method based on data location.Our method directly attack the sensitive points in the time-series data accord-ing to statistical features extract from the dataset.At frst,we have validated the transferability of sensitive points among DNNs with different structures.Secondly,we use the statistical features extract from the dataset and the sensi-tive rate of each point as the training set to train the predictive model.Then,predicting the sensitive rate of test set by predictive model.Finally,perturbing according to the sensitive rate.The attack is limited by constraining the LO norm to achieve one-point attack.We conduct experiments on several datasets to validate the effectiveness of this method.
