Adversarial Stylometry in the Wild: Transferable Lexical Substitution
Attacks on Author Profiling
- URL: http://arxiv.org/abs/2101.11310v1
- Date: Wed, 27 Jan 2021 10:42:44 GMT
- Title: Adversarial Stylometry in the Wild: Transferable Lexical Substitution
Attacks on Author Profiling
- Authors: Chris Emmery, \'Akos K\'ad\'ar, Grzegorz Chrupa{\l}a
- Abstract summary: 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.
- Score: 13.722693312120462
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Written language contains stylistic cues that can be exploited to
automatically infer a variety of potentially sensitive author information.
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, where neither data nor target models are
accessible. We introduce a transformer-based extension of a lexical replacement
attack, and show it achieves high transferability when trained on a weakly
labeled corpus -- decreasing target model performance below chance. While not
completely inconspicuous, our more successful attacks also prove notably less
detectable by humans. Our framework therefore provides a promising direction
for future privacy-preserving adversarial attacks.
Related papers
- Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack [24.954755569786396]
We propose a framework for a broader class of adversarial attacks, designed to perform minor perturbations in machine-generated content to evade detection.
We consider two attack settings: white-box and black-box, and employ adversarial learning in dynamic scenarios to assess the potential enhancement of the current detection model's robustness.
The empirical results reveal that the current detection models can be compromised in as little as 10 seconds, leading to the misclassification of machine-generated text as human-written content.
arXiv Detail & Related papers (2024-04-02T12:49:22Z) - 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) - AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large
Language Models [55.748851471119906]
Safety alignment of Large Language Models (LLMs) can be compromised with manual jailbreak attacks and (automatic) adversarial attacks.
Recent studies suggest that defending against these attacks is possible: adversarial attacks generate unlimited but unreadable gibberish prompts, detectable by perplexity-based filters.
We introduce AutoDAN, an interpretable, gradient-based adversarial attack that merges the strengths of both attack types.
arXiv Detail & Related papers (2023-10-23T17:46:07Z) - Streamlining Attack Tree Generation: A Fragment-Based Approach [39.157069600312774]
We present a novel fragment-based attack graph generation approach that utilizes information from publicly available information security databases.
We also propose a domain-specific language for attack modeling, which we employ in the proposed attack graph generation approach.
arXiv Detail & Related papers (2023-10-01T12:41:38Z) - In and Out-of-Domain Text Adversarial Robustness via Label Smoothing [64.66809713499576]
We study the adversarial robustness provided by various label smoothing strategies in foundational models for diverse NLP tasks.
Our experiments show that label smoothing significantly improves adversarial robustness in pre-trained models like BERT, against various popular attacks.
We also analyze the relationship between prediction confidence and robustness, showing that label smoothing reduces over-confident errors on adversarial examples.
arXiv Detail & Related papers (2022-12-20T14:06:50Z) - 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) - Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of
Language Models [86.02610674750345]
Adversarial GLUE (AdvGLUE) is a new multi-task benchmark to explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.
We apply 14 adversarial attack methods to GLUE tasks to construct AdvGLUE, which is further validated by humans for reliable annotations.
All the language models and robust training methods we tested perform poorly on AdvGLUE, with scores lagging far behind the benign accuracy.
arXiv Detail & Related papers (2021-11-04T12:59:55Z) - Towards Variable-Length Textual Adversarial Attacks [68.27995111870712]
It is non-trivial to conduct textual adversarial attacks on natural language processing tasks due to the discreteness of data.
In this paper, we propose variable-length textual adversarial attacks(VL-Attack)
Our method can achieve $33.18$ BLEU score on IWSLT14 German-English translation, achieving an improvement of $1.47$ over the baseline model.
arXiv Detail & Related papers (2021-04-16T14:37:27Z) - Hidden Backdoor Attack against Semantic Segmentation Models [60.0327238844584]
The emphbackdoor attack intends to embed hidden backdoors in deep neural networks (DNNs) by poisoning training data.
We propose a novel attack paradigm, the emphfine-grained attack, where we treat the target label from the object-level instead of the image-level.
Experiments show that the proposed methods can successfully attack semantic segmentation models by poisoning only a small proportion of training data.
arXiv Detail & Related papers (2021-03-06T05:50:29Z) - Universal Adversarial Attacks with Natural Triggers for Text
Classification [30.74579821832117]
We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems.
Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models.
arXiv Detail & Related papers (2020-05-01T01:58:24Z)
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.