Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling
This work addresses privacy concerns for authors by enabling adversarial attacks in real-world scenarios where data and models are inaccessible, though it is incremental in improving transferability and detectability.
The paper tackles the problem of protecting author privacy by attacking author profiling models through adversarial text rewriting, achieving a decrease in target model performance below chance with attacks that are less detectable by humans.
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.