Tampering, editing, and layout manipulation signals across images, screenshots, scans, and business files.
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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.
Tampering is broader than PDF object edits. Edits hide in pixel-level splices on images, layered overlays on scans, and re-screenshots that strip metadata.
TrueDoc's document tampering detection covers tampered, edited, and manipulated documents — IDs, financial files, contracts, screenshots, and scans — in a single forensic pipeline.
Pixel-level splices on identity, financial, and business documents
Overlay edits on scans (numbers, names, dates)
Layered or composited screenshots
Re-encoded files that hide earlier edits
Metadata inconsistencies indicating later modification
PDF or image, up to 10MB.
ELA, splice detection, copy-move, font/spacing consistency, and metadata analysis.
Tampering findings are reconciled with AI-generation and structural checks.
Approve, hold for review, or reject with evidence.
Document tampering detection is the practice of inspecting a document's pixels, structure, fonts, and metadata to decide whether it was edited, spliced, or manipulated after creation.
Traditional review compares what a document looks like against what it should look like. Modern document tampering detection adds layers humans can't do reliably: Error Level Analysis (ELA), copy-move and splice detection, font and kerning fingerprinting, PDF object inspection, and metadata edit-trail analysis.
TrueDoc combines all of those into one trust score so reviewers see a single verdict plus the underlying tampered document detection signals.
Pixel-level splices on identity, financial, and business documents — usually visible under ELA even when the rendered image looks clean.
Overlay edits on scans where numbers, names, or dates have been painted over the original content.
Layered or composited screenshots stitched from multiple sources to fabricate a single 'proof' image.
Re-encoded files that hide earlier edits by re-compressing the entire document.
Mismatched fonts, kerning, or row alignment in a single document — a strong edited document detection signal.
Metadata inconsistencies — Producer, Creator, ModDate, EXIF, XMP — that disagree with the document's claimed origin.
Metadata fraud detection inspects PDF metadata, EXIF, XMP, and incremental-update chains for inconsistencies. Missing entries, modified ModDate values, and incremental updates that postdate the document's claimed origin are all surfaced as evidence.
Layout checks evaluate column alignment, table structure, and content placement against expected patterns for the document type — useful for catching document manipulation that is structurally inconsistent but visually plausible.
Visual inconsistency checks combine ELA, splice detection, copy-move analysis, and font fingerprinting into one tampered document detection layer. All three layers feed the same trust score and findings list.
No credit card. Redacted report in under a minute.