TY - GEN
T1 - MALCOM
T2 - 20th IEEE International Conference on Data Mining, ICDM 2020
AU - Le, Thai
AU - Wang, Suhang
AU - Lee, Dongwon
N1 - Funding Information:
1This work was in part supported by NSF awards #1742702, #1820609, #1909702, #1915801, #1934782, and #IIS1909702
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11
Y1 - 2020/11
N2 - In recent years, the proliferation of so-called 'fake news' has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art (SOTA) models to autodetect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask 'what if adversaries attempt to attack such detection models?' and investigate related issues by (i) proposing a novel attack scenario against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead SOTA fake news detectors, and (ii) developing Malcom, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average Malcom can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, Malcom can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare our attack model with four baselines across two real-world datasets, not only on attack performance but also on generated quality, coherency, transferability, and robustness. We release the source code of Malcom at https://github.com/lethaiq/MALCOM1.
AB - In recent years, the proliferation of so-called 'fake news' has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art (SOTA) models to autodetect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask 'what if adversaries attempt to attack such detection models?' and investigate related issues by (i) proposing a novel attack scenario against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead SOTA fake news detectors, and (ii) developing Malcom, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average Malcom can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, Malcom can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare our attack model with four baselines across two real-world datasets, not only on attack performance but also on generated quality, coherency, transferability, and robustness. We release the source code of Malcom at https://github.com/lethaiq/MALCOM1.
UR - http://www.scopus.com/inward/record.url?scp=85098219836&partnerID=8YFLogxK
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U2 - 10.1109/ICDM50108.2020.00037
DO - 10.1109/ICDM50108.2020.00037
M3 - Conference contribution
AN - SCOPUS:85098219836
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 282
EP - 291
BT - Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
A2 - Plant, Claudia
A2 - Wang, Haixun
A2 - Cuzzocrea, Alfredo
A2 - Zaniolo, Carlo
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 November 2020 through 20 November 2020
ER -