Synthetic Disinformation Attacks on Automated Fact Verification Systems

• URL: http://arxiv.org/abs/2202.09381v1
• Date: Fri, 18 Feb 2022 19:01:01 GMT
• Title: Synthetic Disinformation Attacks on Automated Fact Verification Systems
• Authors: Yibing Du, Antoine Bosselut, Christopher D. Manning
• Abstract summary: We explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings. We show that these systems suffer significant performance drops against these attacks. We discuss the growing threat of modern NLG systems as generators of disinformation.
• Score: 53.011635547834025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated fact-checking is a needed technology to curtail the spread of online misinformation. One current framework for such solutions proposes to verify claims by retrieving supporting or refuting evidence from related textual sources. However, the realistic use cases for fact-checkers will require verifying claims against evidence sources that could be affected by the same misinformation. Furthermore, the development of modern NLP tools that can produce coherent, fabricated content would allow malicious actors to systematically generate adversarial disinformation for fact-checkers. In this work, we explore the sensitivity of automated fact-checkers to synthetic adversarial evidence in two simulated settings: AdversarialAddition, where we fabricate documents and add them to the evidence repository available to the fact-checking system, and AdversarialModification, where existing evidence source documents in the repository are automatically altered. Our study across multiple models on three benchmarks demonstrates that these systems suffer significant performance drops against these attacks. Finally, we discuss the growing threat of modern NLG systems as generators of disinformation in the context of the challenges they pose to automated fact-checkers.

Related papers

• Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checking [11.891881050619457]
  "CREDible, Unreliable or LEaked" dataset consists of 91,632 articles classified as Credible, Unreliable and Fact checked (Leaked) "EVidence VERification Network (EVVER-Net) trained on CREDULE to detect leaked and unreliable evidence in both short and long texts" "EVVER-Net can demonstrate impressive performance of up to 91.5% and 94.4% accuracy"
  arXiv  Detail & Related papers  (2024-04-29T13:47:04Z)
• Give Me More Details: Improving Fact-Checking with Latent Retrieval [58.706972228039604]
  Evidence plays a crucial role in automated fact-checking. Existing fact-checking systems either assume the evidence sentences are given or use the search snippets returned by the search engine. We propose to incorporate full text from source documents as evidence and introduce two enriched datasets.
  arXiv  Detail & Related papers  (2023-05-25T15:01:19Z)
• Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems [80.3811072650087]
  We show that it is possible to subtly modify claim-salient snippets in the evidence and generate diverse and claim-aligned evidence. The attacks are also robust against post-hoc modifications of the claim. These attacks can have harmful implications on the inspectable and human-in-the-loop usage scenarios.
  arXiv  Detail & Related papers  (2022-09-07T13:39:24Z)
• Attacking Open-domain Question Answering by Injecting Misinformation [116.25434773461465]
  We study the risk of misinformation to Question Answering (QA) models by investigating the sensitivity of open-domain QA models to misinformation documents. Experiments show that QA models are vulnerable to even small amounts of evidence contamination brought by misinformation. We discuss the necessity of building a misinformation-aware QA system that integrates question-answering and misinformation detection.
  arXiv  Detail & Related papers  (2021-10-15T01:55:18Z)
• A Survey on Automated Fact-Checking [18.255327608480165]
  We survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. We present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts.
  arXiv  Detail & Related papers  (2021-08-26T16:34:51Z)
• DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checking [46.13738685855884]
  We show that current systems for fact-checking are vulnerable to three categories of realistic challenges for fact-checking. We present a system designed to be resilient to these "attacks" using multiple pointer networks for document selection. We find that in handling these attacks we obtain state-of-the-art results on FEVER, largely due to improved evidence retrieval.
  arXiv  Detail & Related papers  (2020-04-27T15:18:49Z)
• Generating Fact Checking Explanations [52.879658637466605]
  A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process. This paper provides the first study of how these explanations can be generated automatically based on available claim context. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system.
  arXiv  Detail & Related papers  (2020-04-13T05:23:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.