Writing style functions both as a vehicle of expression and as a marker of authorial identity. Stylometric methods enable automatic recognition of authors based on linguistic regularities, while recent advances in adversarial learning—demonstrate how data can be intentionally modified to prevent models from learning usable representations. Yet it remains unclear whether such perturbations, designed to disrupt machine learning processes, also influence human perception of style.This thesis investigates how humans and models perceive writing style under controlled perturbations and whether manipulations that reduce algorithmic recognition likewise obscure stylistic identity for human readers. The study combines computational and behavioral approaches: constructing semantically controlled yet stylistically diverse text datasets, and conducting human evaluation experiments to compare recognition accuracy between models and readers.The results are expected to clarify how linguistic cues contribute differently to human and algorithmic perception of style and to inform broader applications in authorship analysis, privacy-preserving text transformation, and creative expression. By situating writing style as a dimension of information quality, the research contributes to understanding how authenticity, anonymity, and expressivity interact in digital communication.