Browsing by Author "Shen, Bo"

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A Method of Driving Characteristics Recognition on Vehicle Operation Sequence
(2019-01-01)
Zhang, Yu
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Shen, Bo
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Kao, Yi-Chih
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Chou, Hsin-Hung
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資訊技術服務中心
Driving behavior has been proved to have a great influence on road safety. Recognizing driving characteristic is an essential part of reducing traffic fatalities and developing intelligent traffic system. In this paper, we propose a method of driving characteristics recognition through mining vehicle operation data such as GPS, velocity and direction collected by the On-Board Diagnostic (OBD) port of vehicles. Based on the feature extracted from the vehicle operation sequence, we employ K-means algorithm to cluster and recognize different driving characteristics after reasonable normalization and dimensionality reduction of the features. Analysis and experimental results indicate that the proposed method has good application significance on mining effective information in vehicle operation data sequence.
A New Method to Detect the Adversarial Attack Based on the Residual Image
(2019-01-01)
Sun, Feng
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Zhang, Zhenjiang
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Kao, Yi-Chih
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Li, Tianzhou
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Shen, Bo
Nowadays, with the development of artificial intelligence, deep learning has attracted more and more attention. Whereas deep neural network has made incredible progress in many domains including Computer Vision, Nature Language Processing, etc, recent studies show that they are vulnerable to the adversarial attacks which takes legitimate images with undetected perturbation as input and can mislead the model to predict incorrect outputs. We consider that the key point of the adversarial attack is the undetected perturbation added to the input. It will be of great significance to eliminate the effect of the added noise. Thus, we design a new, efficient model based on residual image which can detect this potential adversarial attack. We design a method to get the residual image which can capture these possible perturbations. Based on the residual image we got, the detection mechanism can help us detect whether it is an adversarial image or not. A serial of experiments has also been carried out. Subsequent experiments prove that the new detection method can detect the adversarial attack with high effectivity.