Adversarial examples detection method based on boundary values invariants
Nowadays,deep learning has become one of the most widely studied and applied technologies in the computer field.Deep neural networks(DNNs) have achieved greatly noticeable success in many applications such as image recognition,speech,self-driving and text translation.However,deep neural networks are...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2020-02-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020012 |
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| _version_ | 1841529940260945920 |
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| author | Fei YAN Minglun ZHANG Liqiang ZHANG |
| author_facet | Fei YAN Minglun ZHANG Liqiang ZHANG |
| author_sort | Fei YAN |
| collection | DOAJ |
| description | Nowadays,deep learning has become one of the most widely studied and applied technologies in the computer field.Deep neural networks(DNNs) have achieved greatly noticeable success in many applications such as image recognition,speech,self-driving and text translation.However,deep neural networks are vulnerable to adversarial examples that are generated by perturbing correctly classified inputs to cause DNN modes to misbehave.A boundary check method based on traditional programs by fitting the distribution to find the invariants in the deep neural network was proposed and it use the invariants to detect adversarial examples.The selection of training sets was irrelevant to adversarial examples.The experiment results show that proposed method can effectively detect the current adversarial example attacks on LeNet,vgg19 model,Mnist,Cifar10 dataset,and has a low false positive rate. |
| format | Article |
| id | doaj-art-101cc44b619f4c159b867a826f622a62 |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2020-02-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-101cc44b619f4c159b867a826f622a622025-01-15T03:13:52ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2020-02-016384559557573Adversarial examples detection method based on boundary values invariantsFei YANMinglun ZHANGLiqiang ZHANGNowadays,deep learning has become one of the most widely studied and applied technologies in the computer field.Deep neural networks(DNNs) have achieved greatly noticeable success in many applications such as image recognition,speech,self-driving and text translation.However,deep neural networks are vulnerable to adversarial examples that are generated by perturbing correctly classified inputs to cause DNN modes to misbehave.A boundary check method based on traditional programs by fitting the distribution to find the invariants in the deep neural network was proposed and it use the invariants to detect adversarial examples.The selection of training sets was irrelevant to adversarial examples.The experiment results show that proposed method can effectively detect the current adversarial example attacks on LeNet,vgg19 model,Mnist,Cifar10 dataset,and has a low false positive rate.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020012deep neuron networkboundary checkinginvariantadversarial examples detecting |
| spellingShingle | Fei YAN Minglun ZHANG Liqiang ZHANG Adversarial examples detection method based on boundary values invariants 网络与信息安全学报 deep neuron network boundary checking invariant adversarial examples detecting |
| title | Adversarial examples detection method based on boundary values invariants |
| title_full | Adversarial examples detection method based on boundary values invariants |
| title_fullStr | Adversarial examples detection method based on boundary values invariants |
| title_full_unstemmed | Adversarial examples detection method based on boundary values invariants |
| title_short | Adversarial examples detection method based on boundary values invariants |
| title_sort | adversarial examples detection method based on boundary values invariants |
| topic | deep neuron network boundary checking invariant adversarial examples detecting |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2020012 |
| work_keys_str_mv | AT feiyan adversarialexamplesdetectionmethodbasedonboundaryvaluesinvariants AT minglunzhang adversarialexamplesdetectionmethodbasedonboundaryvaluesinvariants AT liqiangzhang adversarialexamplesdetectionmethodbasedonboundaryvaluesinvariants |