Document Forgery Detection

Document forgery detection is the identification of documents that are not what they claim: fully fabricated documents built from templates or generators, genuine documents altered after the fact (an edited salary figure, a substituted name), and synthetic composites assembled from parts of real documents. The threat is structural to any process that grants value on documentary evidence — lending, onboarding, claims, immigration — and it has industrialized: template marketplaces sell realistic payslips and statements, editing tools leave ever-fainter traces, and generative AI now produces convincing document images from prompts.

Detection layers multiple forensic lenses, because forgers rarely defeat all of them at once. Digital forensics reads what files disclose about themselves: metadata inconsistencies (creation tools, timestamps, edit histories), PDF internal structure anomalies, compression fingerprints that betray regional re-editing. Image forensics examines pixels: copy-move and splice artifacts, resampling traces, noise patterns that differ where content was inserted, font and kerning inconsistencies invisible to the eye. Content forensics checks the document against reality: checksums and formats of identifiers, arithmetic that must reconcile, cross-document consistency within a case, and issuer-specific conventions (this bank's statements never format dates that way). And channel signals — the same template's fingerprint recurring across unrelated applicants — turn individual screening into network detection.

The operational posture is risk-scoring, not verdicts: signals aggregate into a forgery risk score that routes documents — clean ones proceed, suspicious ones divert to specialist review with the specific findings highlighted, and confirmed patterns feed both the models and the fraud-intelligence loop. Two disciplines matter at scale: calibration against base rates (most anomalies are innocent — bad scanners and helpful customers annotating their own statements outnumber fraudsters), and adversarial maintenance — detection quality decays as attackers adapt, making forgery models among the most retraining-hungry components in a document pipeline.