Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
Chris Emmery, Ákos Kádár, Grzegorz Chrupała · 2021
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 transformerbased extension of a lexical replacement attack, and show it achieves high transferability when trained on a weakly labeled corpusdecreasing 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.