PDF-only: structural, metadata, and forensic checks for edited or synthetic PDFs.
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Estimated savings based on replacing a 10–15 minute manual document review with automated TrueDoc analysis.
Proprietary detection scans template variance, metadata drift, pixel-level retouching, and structural anomalies the human eye misses.
Inspects EXIF, software signatures, edit history, and structural fingerprints.
Originals are processed in encrypted memory and removed after analysis. Reports stay redacted by default.
PDFs look immutable but rarely are. Free editors can swap text, dates, signatures, and amounts without obvious visual cues — and AI tools now generate entire PDFs that mimic real documents.
Detecting tampering requires inspecting the PDF's underlying structure, metadata, and pixel-level forensics — not just looking at the rendered page.
Edited dates, names, amounts, or terms
Replaced or forged signatures
Inserted pages or removed clauses
Mismatched fonts or rasterized text overlays
Modified PDF metadata (producer, creation date)
Up to 10MB — multi-page documents fully supported.
Engine inspects PDF objects, metadata, fonts, and incremental updates.
ELA and tamper detection highlight regions that were edited or spliced.
Verdict, evidence map, and exportable PDF for your case file.
TrueDoc works as a fake PDF detector and fake PDF checker in one pass: it inspects the PDF object graph, font dictionaries, incremental updates, and embedded media to surface edits, then evaluates AI-generation signatures common to fully synthetic PDFs.
Whether you're checking a tampered contract, an edited invoice, or a fully AI-generated bank statement PDF, the same fake PDF detector pipeline returns a Document Trust Score and per-region evidence. For broader (non-PDF) tampering, see document tampering detection.
PDF metadata — Producer, Creator, CreationDate, ModDate, XMP — and the underlying object structure are some of the strongest fraud signals when treated correctly. TrueDoc surfaces inconsistent metadata, missing entries, and incremental-update chains that betray after-the-fact edits.
Structural inconsistencies — mismatched font dictionaries, orphaned objects, irregular cross-reference tables, conflicting content streams — are evaluated as additional visual manipulation signals and feed directly into the trust score.
PDFs are routinely tampered after the fact — text replaced, dates shifted, amounts changed, signatures rasterized over the original. TrueDoc's PDF tampering detection inspects the PDF object graph, incremental updates, font dictionaries, and embedded images to surface edits that the rendered page hides.
Edited PDF detection covers both subtle changes (single-character edits, modified totals) and structural changes (inserted pages, removed clauses).
Current-generation AI tools can produce entire PDFs that mimic real bank statements, invoices, contracts, and certificates. TrueDoc's AI-generated PDF detection evaluates layout regularities, font fingerprinting, content-vs-structure consistency, and generative-AI signatures embedded in rendered pages.
PDF metadata — Producer, Creator, CreationDate, ModDate, XMP — is one of the strongest fraud signals when treated correctly. TrueDoc surfaces inconsistent metadata, missing entries, and incremental-update chains that betray after-the-fact edits.
Structural inconsistencies — mismatched font dictionaries, orphaned objects, irregular cross-reference tables, conflicting content streams — are also evaluated as visual manipulation signals.
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