Classification Score Approach for Detecting Adversarial Example in Deep Neural Network

Cited 20 time in webofscience Cited 0 time in scopus
  • Hit : 369
  • Download : 169
Deep neural networks (DNNs) provide superior performance on machine learning tasks such as image recognition, speech recognition, pattern analysis, and intrusion detection. However, an adversarial example, created by adding a little noise to an original sample, can cause misclassification by a DNN. This is a serious threat to the DNN because the added noise is not detected by the human eye. For example, if an attacker modifies a right-turn sign so that it misleads to the left, autonomous vehicles with the DNN will incorrectly classify the modified sign as pointing to the left, but a person will correctly classify the modified sign as pointing to the right. Studies are under way to defend against such adversarial examples. The existing method of defense against adversarial examples requires an additional process such as changing the classifier or modifying input data. In this paper, we propose a new method for detecting adversarial examples that does not invoke any additional process. The proposed scheme can detect adversarial examples by using a pattern feature of the classification scores of adversarial examples. We used MNIST and CIFAR10 as experimental datasets and Tensorflow as a machine learning library. The experimental results show that the proposed method can detect adversarial examples with success rates: 99.05% and 99.9% for the untargeted and targeted cases in MNIST, respectively, and 94.7% and 95.8% for the untargeted and targeted cases in CIFAR10, respectively.
Publisher
SPRINGER
Issue Date
2021-03
Language
English
Article Type
Article
Citation

MULTIMEDIA TOOLS AND APPLICATIONS, v.80, no.7, pp.10339 - 10360

ISSN
1380-7501
DOI
10.1007/s11042-020-09167-z
URI
http://hdl.handle.net/10203/282221
Appears in Collection
CS-Journal Papers(저널논문)
Files in This Item
000591261400001.pdf(2.92 MB)Download
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 20 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0