Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks

• URL: http://arxiv.org/abs/2403.15467v1
• Date: Wed, 20 Mar 2024 06:28:09 GMT
• Title: Don't be a Fool: Pooling Strategies in Offensive Language Detection from User-Intended Adversarial Attacks
• Authors: Seunguk Yu, Juhwan Choi, Youngbin Kim,
• Abstract summary: Malicious users often attempt to avoid filtering systems through the involvement of textual noises. We propose these evasions as user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. We introduce simple yet effective pooling strategies in a layer-wise manner to defend against the proposed attacks.
• Score: 7.480124826347168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filtering systems through the involvement of textual noises. In this paper, we propose these evasions as user-intended adversarial attacks that insert special symbols or leverage the distinctive features of the Korean language. Furthermore, we introduce simple yet effective pooling strategies in a layer-wise manner to defend against the proposed attacks, focusing on the preceding layers not just the last layer to capture both offensiveness and token embeddings. We demonstrate that these pooling strategies are more robust to performance degradation even when the attack rate is increased, without directly training of such patterns. Notably, we found that models pre-trained on clean texts could achieve a comparable performance in detecting attacked offensive language, to models pre-trained on noisy texts by employing these pooling strategies.

Related papers

• Learning diverse attacks on large language models for robust red-teaming and safety tuning [126.32539952157083]
  Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe deployment of large language models. We show that even with explicit regularization to favor novelty and diversity, existing approaches suffer from mode collapse or fail to generate effective attacks. We propose to use GFlowNet fine-tuning, followed by a secondary smoothing phase, to train the attacker model to generate diverse and effective attack prompts.
  arXiv  Detail & Related papers  (2024-05-28T19:16:17Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers [25.94356063000699]
  Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data. We focus on more realistic and more challenging clean-label attacks where the adversarial training examples are correctly labeled. Our attack, LLMBkd, leverages language models to automatically insert diverse style-based triggers into texts.
  arXiv  Detail & Related papers  (2023-10-28T06:11:07Z)
• Adversarial Text Normalization [2.9434930072968584]
  Adversarial Text Normalizer restores baseline performance on attacked content with low computational overhead. We find that text normalization provides a task-agnostic defense against character-level attacks.
  arXiv  Detail & Related papers  (2022-06-08T19:44:03Z)
• Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
  Adversarial attacks perturb images such that a deep neural network produces incorrect classification results. A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check. We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
  arXiv  Detail & Related papers  (2022-03-29T04:33:06Z)
• Identifying Adversarial Attacks on Text Classifiers [32.958568467774704]
  In this paper, we analyze adversarial text to determine which methods were used to create it. Our first contribution is an extensive dataset for attack detection and labeling. As our second contribution, we use this dataset to develop and benchmark a number of classifiers for attack identification.
  arXiv  Detail & Related papers  (2022-01-21T06:16:04Z)
• Learning-based Hybrid Local Search for the Hard-label Textual Attack [53.92227690452377]
  We consider a rarely investigated but more rigorous setting, namely hard-label attack, in which the attacker could only access the prediction label. Based on this observation, we propose a novel hard-label attack, called Learning-based Hybrid Local Search (LHLS) algorithm. Our LHLS significantly outperforms existing hard-label attacks regarding the attack performance as well as adversary quality.
  arXiv  Detail & Related papers  (2022-01-20T14:16:07Z)
• Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning [50.67997309717586]
  We propose a poisoning attack in which a malicious adversary inserts a small poisoned sample of monolingual text into the training set of a system trained using back-translation. This sample is designed to induce a specific, targeted translation behaviour, such as peddling misinformation. We present two methods for crafting poisoned examples, and show that only a tiny handful of instances, amounting to only 0.02% of the training set, is sufficient to enact a successful attack.
  arXiv  Detail & Related papers  (2021-07-12T08:07:09Z)
• Towards Defending against Adversarial Examples via Attack-Invariant Features [147.85346057241605]
  Deep neural networks (DNNs) are vulnerable to adversarial noise. adversarial robustness can be improved by exploiting adversarial examples. Models trained on seen types of adversarial examples generally cannot generalize well to unseen types of adversarial examples.
  arXiv  Detail & Related papers  (2021-06-09T12:49:54Z)
• Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling [13.722693312120462]
  Adversarial stylometry intends to attack such models by rewriting an author's text. Our research proposes several components to facilitate deployment of these adversarial attacks in the wild.
  arXiv  Detail & Related papers  (2021-01-27T10:42:44Z)

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.