Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation

• URL: http://arxiv.org/abs/2403.11265v2
• Date: Tue, 29 Oct 2024 13:01:18 GMT
• Title: Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation
• Authors: Silvia Corbara, Alejandro Moreo,
• Abstract summary: Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, or by imitating the style of another author.
• Score: 52.72682366640554
• License:
• Abstract: Authorship Verification (AV) is a text classification task concerned with inferring whether a candidate text has been written by one specific author or by someone else. It has been shown that many AV systems are vulnerable to adversarial attacks, where a malicious author actively tries to fool the classifier by either concealing their writing style, or by imitating the style of another author. In this paper, we investigate the potential benefits of augmenting the classifier training set with (negative) synthetic examples. These synthetic examples are generated to imitate the style of the author of interest. We analyze the improvements in classifier prediction that this augmentation brings to bear in the task of AV in an adversarial setting. In particular, we experiment with three different generator architectures (one based on Recurrent Neural Networks, another based on small-scale transformers, and another based on the popular GPT model) and with two training strategies (one inspired by standard Language Models, and another inspired by Wasserstein Generative Adversarial Networks). We evaluate our hypothesis on five datasets (three of which have been specifically collected to represent an adversarial setting) and using two learning algorithms for the AV classifier (Support Vector Machines and Convolutional Neural Networks). This experimentation has yielded negative results, revealing that, although our methodology proves effective in many adversarial settings, its benefits are too sporadic for a pragmatical application.

Related papers

• On Adversarial Examples for Text Classification by Perturbing Latent Representations [0.0]
  We show that deep learning is vulnerable to adversarial examples in text classification. This weakness indicates that deep learning is not very robust. We create a framework that measures the robustness of a text classifier by using the gradients of the classifier.
  arXiv  Detail & Related papers  (2024-05-06T18:45:18Z)
• Adversarial Attacks and Dimensionality in Text Classifiers [3.4179091429029382]
  Adversarial attacks on machine learning algorithms have been a key deterrent to the adoption of AI in many real-world use cases. We study adversarial examples in the field of natural language processing, specifically text classification tasks.
  arXiv  Detail & Related papers  (2024-04-03T11:49:43Z)
• Verifying the Robustness of Automatic Credibility Assessment [50.55687778699995]
  We show that meaning-preserving changes in input text can mislead the models. We also introduce BODEGA: a benchmark for testing both victim models and attack methods on misinformation detection tasks. Our experimental results show that modern large language models are often more vulnerable to attacks than previous, smaller solutions.
  arXiv  Detail & Related papers  (2023-03-14T16:11:47Z)
• Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence [26.301784771724954]
  Deep neural networks are vulnerable to adversarial attacks. In this paper, we take the role of investigators who want to trace the attack and identify the source. We propose a two-stage separate-and-trace framework.
  arXiv  Detail & Related papers  (2022-12-31T01:38:02Z)
• Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers [49.50163349643615]
  In this paper, we propose a gradient-based adversarial attack against transformer-based text classifiers. Experimental results demonstrate that, while our adversarial attack maintains the semantics of the sentence, it can reduce the accuracy of GPT-2 to less than 5%.
  arXiv  Detail & Related papers  (2022-03-11T14:37:41Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• AES Systems Are Both Overstable And Oversensitive: Explaining Why And Proposing Defenses [66.49753193098356]
  We investigate the reason behind the surprising adversarial brittleness of scoring models. Our results indicate that autoscoring models, despite getting trained as "end-to-end" models, behave like bag-of-words models. We propose detection-based protection models that can detect oversensitivity and overstability causing samples with high accuracies.
  arXiv  Detail & Related papers  (2021-09-24T03:49:38Z)
• A Differentiable Language Model Adversarial Attack on Text Classifiers [10.658675415759697]
  We propose a new black-box sentence-level attack for natural language processing. Our method fine-tunes a pre-trained language model to generate adversarial examples. We show that the proposed attack outperforms competitors on a diverse set of NLP problems for both computed metrics and human evaluation.
  arXiv  Detail & Related papers  (2021-07-23T14:43:13Z)
• Adversarial Example Games [51.92698856933169]
  Adrial Example Games (AEG) is a framework that models the crafting of adversarial examples. AEG provides a new way to design adversarial examples by adversarially training a generator and aversa from a given hypothesis class. We demonstrate the efficacy of AEG on the MNIST and CIFAR-10 datasets.
  arXiv  Detail & Related papers  (2020-07-01T19:47:23Z)
• Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers [0.0]
  An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models. We use a fine-tuning of a language model for adversarial attacks as a generator of adversarial examples. Our model works for diverse datasets on bank transactions, electronic health records, and NLP datasets.
  arXiv  Detail & Related papers  (2020-06-19T11:25:36Z)

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.