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.
Technical methods and signals used to detect PDF fraud
Detecting fraudulent PDFs relies on a combination of file-level inspection and content analysis. At the file level, a thorough examination of metadata—such as creation and modification timestamps, author entries, and XMP metadata—can reveal inconsistencies that suggest tampering. Many forgeries involve editing an existing document rather than recreating it from scratch; examining incremental updates and cross-referencing modification dates with embedded timestamps often exposes impossible timelines.
Textual and structural analysis focuses on the internal composition of the PDF. PDFs store content in object streams, and anomalies like duplicated objects, abnormal compression patterns, or unusual font substitutions can indicate cut-and-paste edits or automated generation. Optical character recognition (OCR) combined with layout analysis helps detect when text has been rasterized, overlaid, or stitched from different sources. For example, a scanned signature that is inconsistent with the document’s text layer will appear as an image while other text remains selectable; this mismatch is a strong signal of manipulation.
Image-level forensic techniques examine embedded images for signs of cloning, resampling, or inconsistent noise profiles. Error level analysis and pixel-level comparisons can reveal regions that have different compression histories or have been pasted from another image. Digital signature verification is another pillar: checking certificate validity, chain of trust, and whether the signed byte-range covers the whole document uncovers signatures that were copied or not applied correctly. Hashing and checksums provide a baseline for integrity checks; when available, comparing a document’s hash to a known good value is definitive.
Combining these signals with machine learning increases detection accuracy. Models trained on known tampered and genuine documents learn subtle patterns—like improbable font mixing or improbable spacing—that rule-based checks might miss. Automated pipelines can flag suspicious PDFs for human review, reducing false positives while ensuring that the most significant anomalies receive deeper forensic attention.
Practical workflow, integrations, and real-world examples
A practical fraud-detection workflow begins at ingestion: an Upload interface or API accepts PDFs from users or connected storage providers. Automated pre-processing extracts metadata, renders pages for visual analysis, and runs OCR to create searchable text. The detection engine then applies layered checks—metadata consistency, text/structure validation, image forensics, and digital signature validation—before compiling findings into a report. This end-to-end process supports integration points like dashboards for analysts, webhooks for real-time alerts, and APIs for bulk processing, enabling organizations to embed checks into existing document flows.
Real-world examples demonstrate the value of rigorous PDF inspection. In procurement fraud, attackers often alter invoice amounts or banking details. Forensic analysis can detect altered numeric fields by spotting inconsistent fonts, embedded images used to replace numbers, or timestamp mismatches that indicate late-stage edits. Academic credential fraud often involves scanned certificates with pasted endorsements; image forensics will show inconsistent resolution or duplicated signature pixels. In legal contexts, tampered contracts might hide amendments via invisible text layers or manipulated incremental updates; verifying the certificate chain and byte-range of digital signatures protects against such attacks.
Case studies show that combining automation with human review yields the best outcomes. A financial services firm reduced payment fraud by integrating an automated PDF check into their accounts payable workflow: flagged invoices were routed to a compliance team who confirmed a subset of cases, preventing large-scale losses. Another example in HR: automated checks on submitted resumes and certificates caught several doctored qualifications before hiring decisions were finalized. To ensure admissibility in legal proceedings, maintain chain-of-custody logs, store original files with timestamps, and preserve extracted artifacts like rendered images and signature certificates.
For organizations seeking to detect fraud in pdf, it is critical to balance speed and depth: instant checks for obvious anomalies combined with deeper forensic processes for high-risk documents. Implement configurable thresholds to manage noise, log all analyses for auditability, and design a process that escalates complex findings to trained investigators for final determination.
Gdańsk shipwright turned Reykjavík energy analyst. Marek writes on hydrogen ferries, Icelandic sagas, and ergonomic standing-desk hacks. He repairs violins from ship-timber scraps and cooks pierogi with fermented shark garnish (adventurous guests only).