From Low-level Artifacts to Forgery Knowledge via Semantic Differencing

Making forgery evidence explainable is fundamental to trustworthy AI-Generated Image (AIGI) detection, yet low-level forgery artifacts remain semantically elusive to Multimodal Large Language Models (MLLMs), which can hardly articulate such artifacts in language. We present the first study on semanticizing low-level forgery artifacts. At its core, we introduce Subvisual Forgery Semantics (SFS), describing how forgery traces below salient perception manifest as object-grounded semantic effects, and develop Semantic Differencing (SemDiff), a multi-agent pipeline that contrasts an image with its reconstruction and translates pixel residuals into structured semantic descriptions. With SemDiff, we construct TRACE-Bench, the first benchmark for assessing MLLMs on subvisual forgery semantics, curated from multi-source annotations over three reconstruction models, where state-of-the-art MLLMs struggle to describe them. As a strong baseline, we present ForgeryTracer, which preserves forgery-related low-level cues through residual-token supervision and learns to articulate them as SFS through semantic supervision, showing that SFS can be learned while maintaining detection accuracy. Our work makes low-level forgery evidence not only detectable but explainable, opening a low-level perspective for interpretable AIGI forensics. Data and models will be released.