  ChatGPT brings revolutionary social value but also raises concerns about the
misuse of AI-generated text. Consequently, an important question is how to
detect whether texts are generated by ChatGPT or by human. Existing detectors
are built upon the assumption that there are distributional gaps between
human-generated and AI-generated text. These gaps are typically identified
using statistical information or classifiers. Our research challenges the
distributional gap assumption in detectors. We find that detectors do not
effectively discriminate the semantic and stylistic gaps between
human-generated and AI-generated text. Instead, the "subtle differences", such
as an extra space, become crucial for detection. Based on this discovery, we
propose the SpaceInfi strategy to evade detection. Experiments demonstrate the
effectiveness of this strategy across multiple benchmarks and detectors. We
also provide a theoretical explanation for why SpaceInfi is successful in
evading perplexity-based detection. And we empirically show that a phenomenon
called token mutation causes the evasion for language model-based detectors.
Our findings offer new insights and challenges for understanding and
constructing more applicable ChatGPT detectors.
