Researchers introduce a unified scoring framework called RECAF, short for a relative assessment framework that jointly measures robustness, effectiveness, and cross-dataset generalization. Rather than asking whether a detector is accurate, RECAF asks a harder question: how does a detector behave across clean data, degraded data, and entirely unfamiliar manipulation methods, all at once?