Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling

TL;DR

This paper presents a transfer-based adversarial stylometry method using transformer-enhanced lexical substitution to attack author profiling models, achieving high transferability and low human detectability, advancing privacy-preserving attack techniques.

Contribution

Introduces a transformer-based lexical replacement attack framework that works without access to data or target models, improving transferability and stealthiness in adversarial stylometry.

Findings

High transferability of attacks on author profiling models

Reduced detectability by human evaluators

Effective in decreasing target model accuracy below chance

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.