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How Vulnerable Are Automatic Fake News Detection Methods to Adversarial
  Attacks?

How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?

16 July 2021
Camille Koenders
Johannes Filla
Nicolai Schneider
Vinicius Woloszyn
    GNN
ArXiv (abs)PDFHTML

Papers citing "How Vulnerable Are Automatic Fake News Detection Methods to Adversarial Attacks?"

6 / 6 papers shown
Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models
Piotr Przybyła
Euan McGill
Horacio Saggion
AAML
289
7
0
28 Oct 2024
Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection
Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection
Sungwon Park
Sungwon Han
Xing Xie
Jae-Gil Lee
Meeyoung Cha
341
8
0
17 Jun 2024
Fake News in Sheep's Clothing: Robust Fake News Detection Against
  LLM-Empowered Style Attacks
Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style AttacksKnowledge Discovery and Data Mining (KDD), 2023
Jiaying Wu
Bryan Hooi
401
135
0
16 Oct 2023
Verifying the Robustness of Automatic Credibility Assessment
Verifying the Robustness of Automatic Credibility AssessmentNatural Language Processing (NLP), 2023
Piotr Przybyła
A. Shvets
Horacio Saggion
DeLMOAAML
346
12
0
14 Mar 2023
An Adversarial Benchmark for Fake News Detection Models
An Adversarial Benchmark for Fake News Detection Models
Lorenzo Jaime Yu Flores
Sophie Hao
140
16
0
03 Jan 2022
Adversarial Attacks and Defenses for Social Network Text Processing
  Applications: Techniques, Challenges and Future Research Directions
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
I. Alsmadi
Kashif Ahmad
Mahmoud Nazzal
Firoj Alam
Ala I. Al-Fuqaha
Abdallah Khreishah
A. Algosaibi
AAML
187
17
0
26 Oct 2021
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