Fake News Detection via NLP is Vulnerable to Adversarial Attacks

5 January 2019

Papers citing "Fake News Detection via NLP is Vulnerable to Adversarial Attacks"

8 / 8 papers shown

Title
Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection Sungwon Park Sungwon Han Xing Xie Jae-Gil Lee Meeyoung Cha 63 1 0 17 Jun 2024
Single Word Change is All You Need: Designing Attacks and Defenses for Text Classifiers Lei Xu Sarah Alnegheimish Laure Berti-Equille Alfredo Cuesta-Infante K. Veeramachaneni AAML 32 0 0 30 Jan 2024
AI Ethics Issues in Real World: Evidence from AI Incident Database Mengyi Wei Zhixuan Zhou 21 40 0 15 Jun 2022
Automatic Detection of Entity-Manipulated Text using Factual Knowledge Ganesh Jawahar Muhammad Abdul-Mageed L. Lakshmanan DeLMO 31 10 0 19 Mar 2022
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 37 16 0 26 Oct 2021
Adversarial attacks against Bayesian forecasting dynamic models Roi Naveiro AAML 16 4 0 20 Oct 2021
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation Adaku Uchendu Zeyu Ma Thai Le Rui Zhang Dongwon Lee DeLMO 56 125 0 27 Sep 2021
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models Thai Le Suhang Wang Dongwon Lee 55 59 0 01 Sep 2020