The widespread misuse of large language models (LLMs) has made machine-generated text detection (MGTD) a critical task. Existing detection methods are often vulnerable to stylistic noise, which limits their generalization in real-world scenarios and robustness on short texts. Furthermore, many methods generate multiple auxiliary texts through LLM-based rewriting or similar strategies, substantially increasing detection latency and limiting their practicality. To address these challenges, we propose the **DisGAR** detection framework, which strips stylistic noise from the input text to mitigate its impact on detection performance, thereby improving cross-domain and cross-model robustness. Specifically, we first fine-tune the attention layers of the proxy model to capture generation-specific patterns. Then we further introduce a global artifact aggregation mechanism and a contrastive learning strategy based on rewritten texts, enabling the separation of stylistic artifacts from the underlying generation patterns. Experimental results demonstrate that DisGAR outperforms existing state-of-the-art methods in unseen-model and unseen-domain detection. Moreover, DisGAR achieves detection efficiency comparable to Fast-DetectGPT, successfully balancing detection performance and efficiency. Source code is available at https://github.com/m-Clown/DisGAR.git