The human-level fluency achieved by large language models in text generation has intensified the challenge of distinguishing between human-written and AI-generated texts. While current fine-tuned detectors exist, they often lack robustness against adversarial attacks and struggle with out-of-distribution topics, limiting their practical applicability. This study introduces AIDER, a robust and topic-independent AI-generated text detection framework. AIDER leverages the ALBERT model for topic content disentanglement, enhancing transferability to unseen topics. It incorporates an augmentor that generates robust adversarial data for training, coupled with contrastive learning techniques to boost resilience. Comprehensive experiments demonstrate AIDER’s significant superiority over state-of-the-art methods, exhibiting exceptional robustness against adversarial attacks with minimal performance degradation. AIDER consistently achieves high accuracy in non-augmented scenarios and demonstrates remarkable generalizability to unseen topics. These attributes establish AIDER as a powerful and versatile tool for LLM-generated text detection across diverse real-world applications, addressing critical challenges in the evolving landscape of AI-generated content.