Degradation of Stylometric Attribution Accuracy for AI-Generated Text

The growing use of large language models (LLMs) makes it essential to identify which system produced a given text. Existing stylometric detectors perform well on raw outputs; however, users routinely translate, paraphrase, or edit content before release. These transformations can erase the lexical and syntactic cues that support attribution. This study measures the resulting performance drop on a purpose-built benchmark of synthetic responses from two instruction-tuned models, Qwen 2.5 (32B) and Gemma 3 (27B). Each passage is subjected to common editing workflows ranging from machine translation to paraphrasing and grammar-focused rewriting. The edited variants are evaluated using a custom machine-learning classifier. Overall, attribution remains robust, but obfuscation leads to degradation that often renders the text unusable for professional purposes. Although the analysis is limited to data from two LLMs, the controlled setting shows how specific post-editing actions alter stylometric signals. Recognising this sensitivity is essential for academic integrity checks, platform-moderation tools, and the ongoing debate between watermarking and stylometry. The results provide practical guidance on developing attribution methods that remain reliable after routine text updates.