Effective faking of verbal deception detection with target-aligned adversarial attacks

TL;DR

This paper demonstrates that adversarial attacks using large language models can effectively deceive both human and machine deception detection systems, especially when attacks are tailored to the target's evaluation method.

Contribution

It introduces a novel adversarial attack method that targets deception detection models and humans, highlighting the importance of target alignment for robustness.

Findings

Adversarial modifications reduce detection accuracy to chance when aligned with the target.

Misaligned attacks are less effective, with detection remaining significantly above chance.

Language models can be used easily to generate deceptive statements that fool detection systems.

Abstract

Background: Deception detection through analysing language is a promising avenue using both human judgments and automated machine learning judgments. For both forms of credibility assessment, automated adversarial attacks that rewrite deceptive statements to appear truthful pose a serious threat. Methods: We used a dataset of 243 truthful and 262 fabricated autobiographical stories in a deception detection task for humans and machine learning models. A large language model was tasked to rewrite deceptive statements so that they appear truthful. In Study 1, humans who made a deception judgment or used the detailedness heuristic and two machine learning models (a fine-tuned language model and a simple n-gram model) judged original or adversarial modifications of deceptive statements. In Study 2, we manipulated the target alignment of the modifications, i.e. tailoring the attack to whether the statements would be assessed by humans or computer models. Results: When adversarial modifications were aligned with their target, human (d=-0.07 and d=-0.04) and machine judgments (51% accuracy) dropped to the chance level. When the attack was not aligned with the target, both human heuristics judgments (d=0.30 and d=0.36) and machine learning predictions (63-78%) were significantly better than chance. Conclusions: Easily accessible language models can effectively help anyone fake deception detection efforts both by humans and machine learning models. Robustness against adversarial modifications for humans and machines depends on that target alignment. We close with suggestions on advancing deception research with adversarial attack designs and techniques.