Stress-testing Machine Generated Text Detection: Shifting Language
  Models Writing Style to Fool Detectors
Authors: name: Andrea Pedrotti, name: Michele Papucci, name: Cristiano Ciaccio, name: Alessio Miaschi, name: Giovanni Puccetti, name: Felice Dell'Orletta, name: Andrea Esuli

Recent advancements in Generative AI and Large Language Models (LLMs) have
enabled the creation of highly realistic synthetic content, raising concerns
about the potential for malicious use, such as misinformation and manipulation.
Moreover, detecting Machine-Generated Text (MGT) remains challenging due to the
lack of robust benchmarks that assess generalization to real-world scenarios.
In this work, we present a pipeline to test the resilience of state-of-the-art
MGT detectors (e.g., Mage, Radar, LLM-DetectAIve) to linguistically informed
adversarial attacks. To challenge the detectors, we fine-tune language models
using Direct Preference Optimization (DPO) to shift the MGT style toward
human-written text (HWT). This exploits the detectors' reliance on stylistic
clues, making new generations more challenging to detect. Additionally, we
analyze the linguistic shifts induced by the alignment and which features are
used by detectors to detect MGT texts. Our results show that detectors can be
easily fooled with relatively few examples, resulting in a significant drop in
detection performance. This highlights the importance of improving detection
methods and making them robust to unseen in-domain texts.