Home/Guides/Automated Document Tampering Detection
Solution
Manual review catches 1 in 5 sophisticated edits. Automation catches the other 4.
At any meaningful document volume, manual review becomes both a bottleneck and a false-negative machine. TamperCheck automates structural forensics, pixel-level analysis, and metadata inspection on every document - same coverage regardless of volume, verdict latency that stays flat from 10 to 10,000 documents.
- No document storage
- Digital, scanned or photographed
- ~1 minute per verdict
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The problem
When document volume outpaces human reviewers
A skilled manual reviewer takes 5-15 minutes per document and catches roughly 1 in 5 sophisticated edits. At any meaningful volume - a few hundred documents a day - manual review becomes a bottleneck and a false-negative machine.
Automation changes both numbers. TamperCheck runs the same 200+ forensic checks on every document, regardless of volume. Verdict latency stays under a minute whether you submit 10 documents or 10,000. And because the system inspects things humans can't see - PDF font tables, pixel-level noise, producer metadata - the detection rate is dramatically higher than manual review.
Automated tampering detection isn't about replacing your compliance team. It's about pointing them at the documents that actually need attention.
What we detect
Forensic signals that expose fraud
Fully automated pipeline
Submit a document via API; receive a structured verdict, risk score, and region-level findings. No human review needed in the happy path.
Consistent check coverage
Every document gets the same 200+ checks. No 'the reviewer was tired' false negatives, no inconsistent rigor between reviewers.
Sub-minute verdict latency
Verdicts return in about a minute per document, regardless of submission volume. Async webhooks for batch workflows.
Risk score gating
Calibrated 0-100 risk score lets you auto-approve low-risk, auto-flag high-risk, and only send the ambiguous middle to human review.
Plain-English findings
When a document is flagged, the report tells your team exactly what was detected and where - not just 'suspicious.'
Audit trail
Every verdict is logged with the specific checks that triggered it, supporting compliance review and regulatory audit.
The solution
A pipeline that runs structural forensics, CV, and AI adjudication on every file
One REST endpoint. POST a document, get a verdict. Behind it: structural forensics, computer vision, and AI adjudication running on every file, every time.
How it works
- Single API endpoint - no per-document-type configuration
- Sync or async - immediate response or webhook callback
- Auto-routing - approve, flag, or escalate based on risk score
- Batch upload supported - drop a folder, get a report
- SDKs available - Node, Python, Go, plus REST
- Webhook signing - HMAC-signed webhooks for production integrations
- Zero storage - documents processed ephemerally, no retention
Why us
What you won't get elsewhere
Findings, not a score
We name the field that changed and where it sits on the page.
Others hand you a number to justify.
One endpoint, every type
100+ document types, 190+ countries, no per-type setup.
Elsewhere, each new form is a project.
Catches what was never real
A dedicated pass for AI-generated and deepfake documents.
Template matching only catches edits.
Nothing is retained
Analysed in memory, then discarded. Never trained on.
Most services keep documents for QA.
Live in minutes
Published pricing, no minimum. Test it before you talk to us.
Platforms want a contract first.
Sits alongside your stack
Keep Onfido or Jumio for face match. We add the forensics.
Not a rip-and-replace.
FAQ
Common questions
What is automated document tampering detection?
Software that identifies edited, forged, or AI-generated documents without manual review. A typical pipeline runs structural PDF analysis, pixel-level forensics, metadata audit, and AI-generation detection on every submitted document, returning a verdict and risk score in under a minute.
Is automated tampering detection as accurate as manual review?
More accurate on most benchmarks. Manual review catches roughly 1 in 5 sophisticated edits; automated systems catch 9 in 10 because they inspect things humans can't see - PDF font tables, pixel-level noise, producer metadata. The right model is augmentation: automate the bulk, route ambiguous cases to humans.
How do I integrate automated tampering detection into my workflow?
Most teams add a single API call between document upload and human review. Submit the document, get a verdict, branch the workflow: auto-approve below a risk threshold, auto-flag above it, send the middle band to a reviewer. Integration takes under 30 minutes with TamperCheck.
What document types does automated tampering detection support?
Any document type - bank statements, payslips, identity documents, invoices, certificates, contracts. The forensic checks are document-agnostic; specific business logic like running balance arithmetic on statements is applied automatically based on document classification.
Can automated tampering detection run in real time?
Verdict latency is about a minute per document - fast enough for real-time onboarding. For sub-second needs, run the API call asynchronously and use webhooks to update the workflow when the verdict returns.
Or skip the call entirely
We'll match your first top-up in credits, so you can run your own documents through it now.