about : Upload
Drag and drop your PDF or image, or select it manually from your device via the dashboard. You can also connect to our API or document processing pipeline through Dropbox, Google Drive, Amazon S3, or Microsoft OneDrive.
Verify in Seconds
Our system instantly analyzes the document using advanced AI to detect fraud. It examines metadata, text structure, embedded signatures, and potential manipulation.
Get Results
Receive a detailed report on the document's authenticity—directly in the dashboard or via webhook. See exactly what was checked and why, with full transparency.
How modern systems analyze digital documents to reveal tampering
Understanding how systems identify fraudulent PDFs starts with appreciating the layers of data that make up a file. At the surface is the visible content—text, images, and layout. Beneath that lies metadata such as creation and modification timestamps, author fields, software identifiers, and embedded XMP data. Advanced detection tools parse both layers, comparing declared metadata against internal inconsistencies. For example, a PDF that claims to be created in 2018 but contains fonts or references introduced in 2022 raises a clear red flag.
Beyond metadata, automated analysis inspects text structure and typographic signals. Optical character recognition (OCR) is applied to scanned pages to extract raw text and detect mismatches between selectable text and visible glyphs. Inconsistencies between font metrics, character spacing, or line patterns can indicate cut-and-paste edits or layered imaging. Image forensics look for evidence of compositing—clone-stamping, variable JPEG artifacts, or mismatched color profiles—that often accompany forged signatures or doctored receipts.
Embedded digital signatures and certificate chains provide a powerful cryptographic anchor when present. Verification checks whether a signature is intact, whether the signing certificate is trusted, and whether the certificate’s issuance timeline makes sense. Even when a document lacks a signature, behavior analysis inspects structural anomalies such as unusual object streams, suspiciously altered incremental updates, or removed revision histories. Modern AI models add behavioral context, flagging documents that deviate from expected templates or institutional patterns, and scoring the likelihood of manipulation so investigators can prioritize high-risk items quickly.
Step-by-step verification: Upload, analyze, and receive actionable results
Begin by preparing the document for upload: ensure the file is the original export from the source system whenever possible and note any chain-of-custody details. Use the dashboard to Upload the file via drag-and-drop or manual selection. For high-volume environments, connecting cloud storage or an automated pipeline via API accelerates ingestion and preserves provenance. Once uploaded, the system runs an initial triage to determine whether the file is a native PDF, a scanned image, or a compound file with embedded objects.
Verification runs in seconds because multiple analytic engines operate in parallel. First, a metadata extractor enumerates creation and modification history, embedded fonts, and producer identifiers. Next, text and layout parsers compare structural signatures against known templates and expected field patterns. An image forensics engine inspects embedded bitmaps and signature bitmaps for signs of tampering. Finally, a cryptographic verifier checks embedded signatures and certificate validity. These processes produce a composite authenticity score and a prioritized list of findings, allowing rapid decision-making without manual deep-dives.
Results appear in a detailed report where each flagged item includes a clear explanation of why it was flagged, relevant snippets, and suggested next steps. Reports can be viewed in the dashboard or delivered programmatically via webhook for integration into case management systems. For automated validation workflows or third-party integrations, use the dedicated endpoint to detect fraud in pdf and retrieve structured results in JSON. Reports often include visual overlays showing manipulated areas, side-by-side comparisons of original and OCR-extracted text, and a timeline of document edits when available.
Real-world examples and practical case studies that illustrate detection success
Case 1: Forged invoices. A procurement team received an invoice with plausible branding but an altered bank account. Forensics revealed mismatched metadata: the invoice template matched a vendor’s older format, while the embedded JPEG carried higher compression artifacts and a different color profile. OCR extracted a suspiciously inconsistent amount formatting pattern. The composite analysis flagged the document as tampered, preventing a fraudulent payment.
Case 2: Altered contracts. A legal department encountered a signed contract where a payment clause appeared modified. Digital signature verification showed the signature block intact but the incremental update history contained changes after signing—indicating appended edits not covered by the original signature. A report highlighted the unsigned alterations, allowing the signers to revalidate terms and demand corrected execution.
Case 3: Academic credential fraud. An employer received a degree certificate that visually matched an institution’s design. Metadata, however, showed the document was produced by consumer-grade editing software rather than the university’s official generator, and the certificate’s serial number did not appear in the institution’s registry. The detection system’s anomaly scoring prioritized the record for manual follow-up, saving the employer from hiring based on counterfeit credentials.
In each example, the value lies not only in identifying a problem but in presenting transparent, actionable evidence: highlighted areas of concern, clear descriptions of anomalous metadata, and recommended verification steps. Organizations that adopt this approach reduce false positives, streamline investigations, and build defensible audit trails. Using advanced analytics to combine metadata, image forensics, OCR, and signature validation transforms how institutions protect themselves from document fraud without slowing routine workflows.
Beirut architecture grad based in Bogotá. Dania dissects Latin American street art, 3-D-printed adobe houses, and zero-attention-span productivity methods. She salsa-dances before dawn and collects vintage Arabic comic books.