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BODEGA: Benchmark for Adversarial Example Generation in Credibility Assessment

2023-03-14 16:11:47
Piotr Przybyła, Alexander Shvets, Horacio Saggion

Abstract

Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. Having the incentive to evade further detection, content creators try to come up with a slightly modified version of the text (known as an attack with an adversarial example) that exploit the weaknesses of classifiers and result in a different output. Here we introduce BODEGA: a benchmark for testing both victim models and attack methods on four misinformation detection tasks in an evaluation framework designed to simulate real use-cases of content moderation. We also systematically test the robustness of popular text classifiers against available attacking techniques and discover that, indeed, in some cases barely significant changes in input text can mislead the models. We openly share the BODEGA code and data in hope of enhancing the comparability and replicability of further research in this area.

Abstract (translated)

文本分类方法已经被广泛研究作为检测低信誉内容的方法:假新闻、社交媒体机器人、宣传等。相当准确的模型(很可能基于深度学习网络)帮助监管公共电子平台,并常常导致内容创作者面临拒绝其提交或删除已经发布的文本的激励。有逃避进一步检测的激励,内容创作者试图想出一个略微修改的版本(称为攻击具有对抗性示例的攻击)来利用分类器的的弱点,产生不同的输出。在这里,我们介绍了BODEGA:一个基准测试框架,旨在模拟内容 moderation 的实际 use-cases,用于测试受害者模型和攻击方法,在评估框架中模拟了四个虚假信息检测任务。我们还 systematic 地测试了流行的文本分类器对可用攻击技术的鲁棒性,并发现,事实上,输入文本中几乎微小的变化可能会误导模型。我们公开分享了BODEGA 代码和数据,希望增强这一领域的研究可比性和可重复性。

URL

https://arxiv.org/abs/2303.08032

PDF

https://arxiv.org/pdf/2303.08032.pdf


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