Forensic PDF API
HTPBE? is a forensic PDF API. Every request runs the same byte-level analysis a fraud analyst would perform manually — cross-reference table, incremental updates, producer field, font subset prefixes, signature chain, image streams — and returns a structured verdict with named markers. One POST, under 3 seconds, no UI. Built for fraud ops at lenders, insurance carriers, and document-heavy back offices. Self-serve API key at signup, no sales call, free test keys on all plans. From $15/mo.
The problem
Modern document fraud is invisible to visual review
A growing class of document fraud opens a genuine PDF, edits a balance, a date, or a beneficiary, and re-saves it. Visually nothing changes — the document passes pixel-level review, layout review, and KYC.
Structural PDF analysis reads the layers rendering engines never expose: revision history, object structure, signature coverage maps. That is where edits leave fingerprints they cannot wipe.
Common tampering patterns
- Modified balances or totals after export
- Swapped IBAN or beneficiary on invoices
- Post-signature edits on contracts
- Backdated issue and modification dates
- Fabricated documents from consumer PDF tools
What this looks like
What the Forensic PDF API Analyzes
Three real fraud mechanics we catch at the structural PDF layer.
Cross-reference table and incremental updates
Every post-creation edit appends a new section to the PDF cross-reference table. The forensic PDF API counts revision layers and reports how many times the file was re-saved. A bank statement with three xref layers was edited three times after the bank generated it.
Producer and creator field forensics
The API maintains a database of hundreds of PDF tool signatures — institutional generators (banking portals, payroll engines, IRS e-file), consumer editors (iLovePDF, Smallpdf, PDF24), and online forgery tools. When the producer field on a document that should reflect institutional software matches a known editing tool, the API returns the tool name and a high-confidence marker.
Signature chain analysis
The API detects whether a document was digitally signed, whether the signature is still valid, and whether modifications were applied after signing. Post-signature edits and signature removal both return certain-confidence markers — these are the strongest tamper signals in PDF forensics.
Font subset prefix divergence
When a PDF is composed from multiple source files or edited across independent sessions, the font subset prefixes diverge between pages. This is invisible to a reader but is a clean structural signal of composite assembly. The forensic PDF API surfaces this divergence as a named marker.
Image stream and JPEG-level anomalies
Tamper attempts often involve re-encoding raster regions of a PDF. The API inspects JPEG quantization tables and APP markers, flags non-genuine compression patterns, and detects when raster overlays were stamped onto an otherwise digitally-generated document.
Date and metadata consistency
Creation, modification, and metadata-update timestamps are cross-checked. A document where modification follows creation by days, weeks, or months — when the document type should be a single-session institutional export — returns a named marker explaining the gap.
The detection gap
KYC platforms check the document. HTPBE? checks the file.
Two different checks — both matter.
KYC & identity platforms
Plaid · Persona · Alloy · Jumio
- Is this a real bank statement template?
- Does the account number match the identity?
- Is the document format consistent with the issuing bank?
Detects fake documents. Does not detect edited real documents.
HTPBE? tamper detection API
Structural PDF integrity
- Was this specific PDF file modified after it was generated?
- Do metadata timestamps match the file structure?
- Were digital signatures valid at the time of signing?
What HTPBE? checks
Detection capabilities
Deterministic structural signals. No probabilistic scores, no model training.
Producer signature mismatch
The PDF claims to come from one tool but the binary structure points to another. The first signal of post-export editing.
Incremental update trail
Every save after the original creates an incremental update. Long chains mean multiple editing sessions on the same file.
Multiple xref tables
Each editing session adds a new cross-reference table. Genuine institutional PDFs have one. Tampered PDFs have several.
Modification timestamp gap
A real PDF has matching CreationDate and ModDate. Months between them is a high-confidence forgery signal.
Digital signature validation
When a digital signature exists, we verify the coverage map. Modifications after signing return certain-confidence verdicts.
Font and object consistency
Edited text introduces new font subsets or objects with origin patterns inconsistent with the rest of the document.
Share with engineering
Wire this into your intake pipeline in under a day
Two API calls — one POST to submit the PDF, one GET to retrieve the verdict. Forward this page to your engineering team; the full API reference, quotas, and copy-paste examples in cURL, JavaScript, Python, PHP, Go, and Ruby are one click away.
Pricing
Self-serve plans, no sales call
All plans include the same forensic checks. Pick the quota that matches your monthly document volume.
manualStarter
$15/mo
30 checks/mo
Manual spot-checks and integration testing
most commonGrowth
$149/mo
350 checks/mo
Active document processing pipelines
high volumePro
$499/mo
1,500 checks/mo
High-volume automation and API integrations
Enterprise (unlimited, on-premise available) — see full pricing
API key on signup. Free test environment on every plan. No card required.
Customer Stories
Teams that stopped document fraud
Compliance, finance, and risk teams use HTPBE? to catch manipulated PDFs before they become costly mistakes.
Caught an invoice where the total had been changed by less than a thousand dollars. Without this I would have approved it without a second look.
Sarah M.
AP Manager
United States
We had three applicants in the same week with bank statements that looked completely fine. Two of them were flagged as modified. You simply cannot see this by reading the document — it is in the file structure.
Lars V.
Risk Analyst, Online Lending
Netherlands
Salary slips were coming with altered figures. We identified two problematic files before the placement was finalised.
Priya K.
HR Operations Lead
India
Since we started checking documents this way, we stopped two applications early in the process that would have been very difficult to reverse later.
Julien R.
Fraud Analyst, Fintech
France
Some applicants were sending PDFs that looked authentic but had been edited in ways not visible to the eye. We now ask for checked originals when something is flagged. Already saved us from a few bad decisions.
Marta S.
Compliance Coordinator
Spain
One invoice was caught because there was a mismatch between the document dates and structure. That particular case would have cost us significantly.
Tariq A.
Finance Manager
United Arab Emirates
FAQ
Frequently asked questions
How is this different from OCR-based document validation?
What documents can the API analyze?
Do I need to send the original file?
How fast is the response?
What is the pricing?
Secure your workflow
Create your account — API key on signup, free test environment on every plan.
From $15/mo. No sales call. Cancel any time.