Efficient Black-Box Adversarial Attacks on Neural Text Detectors

Understand

Neural text detectors are models trained to detect whether a given text was generated by a language model or written by a human.

• In this paper, we investigate three simple and resource-efficient strategies (parameter tweaking, prompt engineering, and character-level mutations) to alter texts generated by GPT-3.5 that are unsuspicious or unnoticeable for humans but cause misclassification by neural text detectors.
• The results show that especially parameter tweaking and character-level mutations are effective strategies.