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    <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.</abstract>
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%0 Conference Proceedings
%T Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
%A Emmery, Chris
%A Kádár, Ákos
%A Chrupała, Grzegorz
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F emmery-etal-2021-adversarial
%X 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.
%R 10.18653/v1/2021.eacl-main.203
%U https://aclanthology.org/2021.eacl-main.203
%U https://doi.org/10.18653/v1/2021.eacl-main.203
%P 2388-2402

Markdown (Informal)

[Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling](https://aclanthology.org/2021.eacl-main.203) (Emmery et al., EACL 2021)

• Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling (Emmery et al., EACL 2021)

ACL

• Chris Emmery, Ákos Kádár, and Grzegorz Chrupała. 2021. Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2388–2402, Online. Association for Computational Linguistics.