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Detect tampered and deepfake Quotation

A quotation can be altered in minutes with a free PDF editor, and the result passes visual inspection because the layout, letterhead, and typeface are all genuine. Only the values have moved. The edits that matter are almost always small: unit prices and totals, quote validity date, and quote number, changed on an otherwise authentic document. TamperCheck analyses a quotation forensically rather than visually, and names the specific field that was edited.

API slug: quotation

What a genuine quotation contains

Whatever the issuer or jurisdiction, a quotation carries a small set of load-bearing fields: unit prices and totals, quote validity date, quote number, and seller name and bank details. These are the fields a reviewer reads, and the fields a forger edits.

They are also bound to one another and to the file that carries them. Dates sit in a plausible order, identifiers follow the issuer's format, and every field was laid down in a single production pass, so they share one compression history, one font rendering, and one noise profile. A genuine document satisfies all of those relationships at once. An edited one rarely does.

How quotation forgeries are made

  • Altering the unit prices and totals

    The unit prices and totals are overwritten in place while the surrounding page is left untouched. The document still reads correctly, but the edited region no longer shares the compression history and font rendering of the surrounding text, and any total derived from it stops reconciling.

  • Altering the quote validity date

    Shifting the quote validity date makes an old or out-of-scope document read as current. This is the cheapest forgery to attempt, changing only a handful of characters, and the one most often missed by reviewers checking content rather than chronology.

  • Altering the quote number

    Fabricating or transplanting the quote number defeats checks that only confirm an identifier is well-formed. A structurally valid value copied from a real document satisfies format validation while belonging to someone else entirely.

  • Altering the seller name and bank details

    Substituting the seller name and bank details repurposes a genuine document so it describes a different person or organisation than the one it was issued to. Every other element is authentic, which is why visual review tends to pass it.

  • Fully synthetic generation

    The whole document is produced by a generative model or an online template service rather than edited from a real source. Every field is internally consistent, so logical and arithmetic checks pass. These are caught by generation signatures instead.

  • Print and rescan laundering

    The edited file is printed and then photographed or scanned to destroy the digital edit trail. This removes the original metadata, but resampling and reprinting introduce their own detectable artifacts.

What TamperCheck checks on a quotation

TamperCheck runs 200+ forensic checks across three layers and returns a risk score from 0 to 100 with plain-English findings tied to specific regions of the document. These three carry the most weight on this document class.

  • Arithmetic reconciliation

    Stated totals are recomputed from their components, and running or cumulative figures are checked for continuity across periods. A single edited value breaks the chain even when the page still looks right.

  • Table structure integrity

    Inserted and deleted rows leave structural artifacts behind: inconsistent spacing, misaligned columns, and formatting the visible table no longer accounts for.

  • Letterhead, logo, and issuer plausibility

    Issuer branding is checked for resolution and compression consistency against the body text it sits on. A logo lifted from a website carries the signature of its source, not of the document.

  • And many more checks

    The three above are the layers that carry the most weight on a quotation. Every upload runs the full suite of 200+ checks regardless of document class, spanning file structure and metadata, pixel-level forensics, font and text rendering, optical and print characteristics, provenance signals, AI-generation signatures, and many more checks.

Who verifies a quotation, and why

Procurement and finance teams, lenders, and buyers comparing supplier quotes. In each case the document is being used to unlock money, access, or a legal status, which is exactly what makes it worth forging.

How to verify a quotation in 4 steps

  1. Check the fields against each other

    Read unit prices and totals, quote validity date, and quote number together rather than one at a time. Forgers typically change one value and leave the rest of the document describing the original.

  2. Inspect the file metadata

    Open the document properties and look at the producer, creation date, and modification date. A document produced by a consumer PDF editor, or created long after the date printed on its face, is worth a closer look.

  3. Ask for a second document

    Request a corroborating document from the same issuer or an adjacent period. Forgery effort concentrates on one file, so inconsistencies surface as soon as there are two to compare.

  4. Run a forensic check

    Manual review catches obvious edits but not field-level pixel manipulation or synthetic generation. TamperCheck runs 200+ forensic checks on a quotation and returns a risk score from 0 to 100 within seconds, at $0.50 per document.

Frequently asked questions

How do I check whether a supplier quotation is genuine?

Start with the fields most often altered on this document class: unit prices and totals, quote validity date, and quote number. Check that they agree with one another and with the file's own metadata. Edits that survive that review are caught forensically, through per-field compression, font, and generation analysis.

Can a price quote be edited?

Yes. A quotation is only as trustworthy as the file carrying it, and both localised editing and full synthetic generation leave measurable traces. TamperCheck runs 200+ forensic checks and names the field that was altered.

What software detects quotation fraud?

TamperCheck analyses a quotation through a single REST endpoint. Upload a PDF or image and receive a JSON verdict with a risk score from 0 to 100 and findings tied to specific regions of the document. Pricing is $0.50 per document, with no subscription or minimum.

Can an AI-generated quotation be detected?

Yes. Fully synthetic documents are internally consistent, so logical and arithmetic checks pass on them. They are caught instead by generation signatures, because the noise and compression characteristics of generated imagery differ measurably from documents produced by real scanners, cameras, and printers.

TamperCheck analyses quotation uploads with a hybrid forensic and AI pipeline tuned for this document class. Upload endpoints accept PDF and common image formats; class is inferred automatically. See the API documentation for authentication, async jobs, and webhooks.

Related document types

All supported document types