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 analysis exposes manipulated files: metadata, structure, and signatures
PDFs are deceptively editable: what appears as a bona fide document can hide layers of manipulation. At the technical core of verification is metadata analysis. Metadata contains creation dates, modification timestamps, author identifiers, and the software used to generate a file. A document claiming to be decades old but bearing metadata from a recently released PDF editor is an immediate red flag. Advanced verification systems parse these metadata fields and correlate them with expected timelines and known software signatures.
Beyond metadata, the internal structure of a PDF reveals manipulation. PDFs are built from objects: text streams, fonts, images, annotations, and form fields. Inconsistent font embedding, mismatched object IDs, or duplicate text layers (common when pages are pasted from other files) indicate tampering. Systems use heuristics and pattern recognition to flag anomalies like inconsistent compression artifacts or embedded images with separate color profiles—signs that pages may have been inserted from another source.
Cryptographic and visible signatures are another pillar of trust. A valid digital signature follows a chain of trust backed by certificates; verification validates the signature's integrity and checks whether the signing certificate is revoked or expired. Visual signatures (scanned handwritten signatures) require different checks: forensic analysis of the image layer, comparison against known exemplars, and analysis of pressure and stroke continuity when available. Combining these checks with OCR-driven text consistency analysis allows systems to detect altered numeric values, swapped names, or subtle edits. Highlighting suspicious elements using forensic comparison helps investigators focus on critical discrepancies.
Practical verification workflow: upload, instant AI checks, and actionable reports
An effective verification workflow is simple for users but sophisticated behind the scenes. Start with a secure upload mechanism that accepts PDFs and images via drag-and-drop or manual file selection. For organizational needs, integrations to cloud storage—Dropbox, Google Drive, Amazon S3, and Microsoft OneDrive—streamline batch processing. Once uploaded, the system performs an automated pipeline: first extracting metadata and file history, then running OCR to convert embedded images of text into searchable content. This dual approach ensures both the file properties and visible content are scrutinized.
Next, automated AI modules analyze structure and semantics. Structural analysis parses object trees to detect pasted pages, hidden layers, or mismatched fonts. Semantic analysis checks for logical inconsistencies: a notarization date that precedes a referenced contract date, or numeric totals that don’t add up. Signature verification runs in parallel: verifying embedded cryptographic signatures against certificate authorities and checking visual signature authenticity when cryptographic proofs are absent. Wherever possible, anomaly scores are generated to quantify risk.
Results should be presented in a clear, actionable format. A concise dashboard report lists checked elements—metadata, text integrity, signature validity, image tampering—and highlights why each item passed or failed. For developers and enterprise users, the same findings can be delivered through webhooks or API callbacks, enabling automatic downstream actions like quarantine, user notifications, or escalation to human review. When speed is crucial, a quick external check can be performed using tools such as detect fake pdf, which integrates many of these automated checks into a single step.
Real-world cases and best practices: lessons from forgery investigations
High-profile cases show how even well-intentioned parties can be misled by forged or altered PDFs. In one scenario, a loan application contained a forged bank statement: metadata suggested creation by a consumer-level PDF editor, and the embedded image showed inconsistent JPEG compression across bank logo and transaction areas. Investigators used layered image analysis to reveal that the logo had been pasted from a legitimate bank site while transaction lines were newly typed. The mismatch in image color profiles and font embedding made the forgery evident.
Another common case involves forged contracts where a single clause was modified after signing. Visual inspection failed because the document looked authentic, but text-level forensic checks detected inconsistencies in word spacing and typeface metrics. OCR-based cross-checking compared the signed PDF to earlier drafts, revealing that clause numbering had been altered and pagination gaps inserted. This type of manipulation can be detected by comparing cryptographic signatures (if present) or by extracting and comparing version histories when available.
Best practices to minimize risk include enforcing digital signatures from trusted certificate authorities, using secure document workflows that lock down versions after signing, and enabling automated verification at intake points (HR, finance, legal). Regular training on spotting red flags—odd metadata, mismatched fonts, image artifacts, and inconsistent dates—empowers staff to escalate suspicious files. For organizations processing high volumes of documents, integrating automated scanners into the document pipeline ensures every upload is verified, with clear, auditable reports that explain why a file was flagged. Combining technology with procedural controls creates a resilient defense against forged PDFs, protecting decisions, finances, and reputations.
Kraków-born journalist now living on a remote Scottish island with spotty Wi-Fi but endless inspiration. Renata toggles between EU policy analysis, Gaelic folklore retellings, and reviews of retro point-and-click games. She distills her own lavender gin and photographs auroras with a homemade pinhole camera.