TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Adversarial Detection with Gaussian Process Regression-based Detector |
| 영문제목(English Title) |
Adversarial Detection with Gaussian Process Regression-based Detector |
| 저자(Author) |
Sangheon Lee
Noo-ri Kim
Youngwha Cho
Jae-Young Choi
Suntae Kim
Jeong-Ah Kim
Jee-Hyong Lee
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| 원문수록처(Citation) |
VOL 13 NO. 08 PP. 4285 ~ 4299 (2019. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model. In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression. Existing deep learning-based adversarial detection methods require numerous adversarial images for their training. The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images. This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model. Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks. In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.
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| 키워드(Keyword) |
Adversarial Attack
Adversarial Defense
Adversarial Detection
Gaussian Process Regression
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| 파일첨부 |
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