Towards a Robust Deep Neural Network Against Adversarial Texts: A Survey

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AHZUTKZCW" target="_blank" >RIV/00216208:11320/23:HZUTKZCW - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118115176&doi=10.1109%2fTKDE.2021.3117608&partnerID=40&md5=49d7f261bcfc6933213668123cfc6c27" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118115176&doi=10.1109%2fTKDE.2021.3117608&partnerID=40&md5=49d7f261bcfc6933213668123cfc6c27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/tkde.2021.3117608" target="_blank" >10.1109/tkde.2021.3117608</a>

Result language

angličtina

Original language name

Towards a Robust Deep Neural Network Against Adversarial Texts: A Survey

Original language description

"Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing (NLP)). However, researchers have demonstrated that DNN-based models are vulnerable to adversarial examples, which cause erroneous predictions by adding imperceptible perturbations into legitimate inputs. Recently, studies have revealed adversarial examples in the text domain, which could effectively evade various DNN-based text analyzers and further bring the threats of the proliferation of disinformation. In this paper, we give a comprehensive survey on the existing studies of adversarial techniques for generating adversarial texts written by both English and Chinese characters and the corresponding defense methods. More importantly, we hope that our work could inspire future studies to develop more robust DNN-based text analyzers against known and unknown adversarial techniques. We classify the existing adversarial techniques for crafting adversarial texts based on the perturbation units, helping to better understand the generation of adversarial texts and build robust models for defense. In presenting the taxonomy of adversarial attacks and defenses in the text domain, we introduce the adversarial techniques from the perspective of different NLP tasks. Finally, we discuss the existing challenges of adversarial attacks and defenses in texts and present the future research directions in this emerging and challenging field. © 1989-2012 IEEE."

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

"IEEE Transactions on Knowledge and Data Engineering"

ISSN

1041-4347

e-ISSN

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Volume of the periodical

35

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

21

Pages from-to

3159-3179

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85118115176