Trust the ID, Question the Logic: Rethinking ID Verification

Identity documents are the most important line of defense between a verified citizen and an impersonator, and that line is breaking in ways we have not yet learned to see. The global identity verification ecosystem has long anchored its fraud detection capabilities on what can be seen: pixel-level anomalies, morphed photographs, spliced regions, and font irregularities. These are real threats, and the community has built real defenses. But the adversary has moved on. A new class of document fraud, logical manipulation, has quietly outpaced every existing defense. These forgeries carry no visual trace, no textual aberration, and no compression artifact. Instead, they embed contradictions into the semantic fabric of the document itself. They are, by every perceptual measure, perfect fakes. This paper presents the first systematic study of logical manipulation in Aadhaar cards, $\text{proactively}$ anticipating emerging threats before they become widespread or exploitable at scale. We introduce $\text{FactID}$, the largest Aadhaar card manipulation dataset, comprising 78,702 samples spanning a rigorously defined taxonomy of template-level and content-level logical manipulations, alongside visual face-level manipulation. Through exhaustive benchmarking of image forensic classifiers, recent manipulation detectors, and state-of-the-art document verification models, we demonstrate that every evaluated system fails, showing that logical manipulation is not a corner case. It is an open frontier, and this work is its first map.