Title: Black-box Detection of LLM-generated Text Using Generalized Jensen-Shannon Divergence
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Text Detection, Likelihood-free Hypothesis Testing, Generalized Jensen-Shannon Divergence

Abstract: We study black-box detection of machine-generated text under practical constraints: the scoring model (proxy LM) may mismatch the unknown source model, and per-input contrastive generation is costly. We propose SurpMark, a reference-based detector that summarizes a passage by the dynamics of its token surprisals. SurpMark quantizes surprisals into interpretable states, estimates a state-transition matrix for the test text, and scores it via a generalized Jensen–Shannon (GJS) gap between the test transitions and two fixed references (human vs. machine) built once from historical corpora. We prove a principled discretization criterion and establish the asymptotic normality of the decision statistic. Empirically, across multiple datasets, source models, and scenarios, SurpMark consistently matches or surpasses baselines; our experiments corroborate the statistic’s asymptotic normality, and ablations validate the effectiveness of the proposed discretization.

TL;DR: We propose a reference-based detector using surprisal-state Markov transitions and GJS score to flag AI text—fast, accurate, no regeneration.