With the rapid proliferation of large language models (LLMs), their misuse has engendered significant societal concern. Accordingly, the development of efficient and robust AI-generated text detection has emerged as a pivotal strategy for mitigating the potential abuse of LLMs. Existing approaches predominantly rely on a compute-intensive fine-tuning paradigm to capture the implicit stylistic cues of AI-generated text. However, fine-tuning such detectors not only incurs substantial overhead but also yields poor robustness, as classification based solely on stylistic cues fails against textual adversarial attack. This paper introduces ColorFP, a robust AI-generated text detection framework based on fixed vocabulary partitioning and half-bit fingerprinting. Specifically, to achieve the optimal trade-off between detection success rates and generated text quality, we introduce a novel probabilistically biased half-bit fingerprint encoding. To enhance detection robustness, we employ a static hash-seeded pseudorandom number generator to ensure consistent vocabulary partitioning across distinct fingerprints, thereby mitigating the challenges posed by textual adversarial attacks. To comprehensively evaluate ColorFP, we assembled a corpus of fingerprinted text outputs from five LLMs; results show that ColorFP outperforms all baselines–achieving a 93.70% average F1 in a five-class setting–while reducing time and computational overhead by up to 30 \(\times \) compared to state-of-the-art approaches.