Adversarial Examples Generation Approach for Tendency Classification on Chinese Texts

doi:10.13328/j.cnki.jos.005765

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2025-5-2- 7

Home > Archive>Volume 30, Issue 8, 2019 >2415-2427. DOI:10.13328/j.cnki.jos.005765

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Adversarial Examples Generation Approach for Tendency Classification on Chinese Texts
DOI:
                        10.13328/j.cnki.jos.005765
                    
Author:
                        WANG Wen-QiWANG Wen-Qi
Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University), Ministry of Education, Wuhan 430072, China;School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
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WANG RunWANG Run
Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University), Ministry of Education, Wuhan 430072, China;School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
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WANG Li-NaWANG Li-Na
Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University), Ministry of Education, Wuhan 430072, China;School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
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Tang Ben-XiaoTang Ben-Xiao
Key Laboratory of Aerospace Information Security and Trusted Computing(Wuhan University), Ministry of Education, Wuhan 430072, China;School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China
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Affiliation:
Clc Number:TP309
Fund Project:National Natural Science Foundation of China (61876134); National Key Research and Development Program of China (2016YFB0801100); Fundamental Research Funds for the Central Universities (2042018kf1028)

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Abstract:

Studies have shown that the adversarial example attack is that small perturbations are added on the input to make deep neural network (DNN) misbehave. Meanwhile, these attacks also exist in Chinese text sentiment orientation classification based on DNN and a method "WordHandling" is proposed to generate this kind of adversarial examples. This method designs a new algorithm aiming at calculating important words. Then the words are replaced with homonym to generate adversarial examples, which are used to conduct an adversarial example attack in black-box scenario. This study also verifies the effectiveness of the proposed method with real data set, i.e. Jingdong shopping and Ctrip hotel review, on long short-term memory network (LSTM) and convolutional neural network (CNN). The experimental results show that the adversarial examples in this study can mislead Chinese text orientation detection system well.

Key words:Chinese text;adversarial examples;deep learning models;score function;black box

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王文琦,汪润,王丽娜,唐奔宵.面向中文文本倾向性分类的对抗样本生成方法.软件学报,2019,30(8):2415-2427

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Received:May 31,2018
Revised:September 21,2018
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Online: April 03,2019
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