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 advanced AI analyzes PDFs to reveal fraud
Detecting fraudulent PDFs requires more than a quick visual scan; it demands a deep technical inspection of both visible content and hidden artifacts. Modern solutions rely on a combination of metadata analysis, structural parsing, and machine learning models trained to spot anomalies. Metadata such as creation and modification timestamps, software identifiers, and author fields often tell the first story; mismatches between declared creation dates and embedded timestamps are common red flags. Advanced tools parse the PDF object structure to locate hidden streams, unused objects, or suspicious references that indicate editing or grafted content.
Text-level inspection includes checking font embedding, text extraction order, and logical reading order. Fraudsters sometimes overlay new text with identical fonts or paste scanned images that contain altered content. Optical character recognition (OCR) combined with semantic analysis can compare extracted text against expected templates or known legitimate documents to flag irregularities. Image-level checks examine compression artifacts, inconsistent DPI settings, and evidence of splicing—areas where an image was composited from multiple sources.
Cryptographic validation is another pillar. Digitally signed PDFs should be validated against the embedded certificate chain and revocation lists. A valid signature confirms both content integrity and signer identity, while a broken chain or modified signed content signals tampering. AI enhances these techniques by correlating multiple indicators—metadata inconsistencies, signature anomalies, OCR mismatches, and unusual object graphs—into a unified risk score, enabling quick prioritization and clear explanations of why a file is suspicious.
Practical steps and tools to verify PDF authenticity
Start every verification process with a controlled upload environment, ensuring the file is transferred without intermediate automated conversions that might strip metadata. Use a dashboard or a secure API connection to retain original file properties. Once uploaded, perform an initial checksum or hash comparison to detect any mid-transit modification. Next, extract and examine PDF metadata for timestamps, author fields, and the producing application. An innocuous-looking name like "invoice.pdf" can hide a complex history of edits from multiple, inconsistent sources.
Run OCR on scanned pages and compare the machine-readable text with the visible content; discrepancies often reveal pasted corrections or image-based edits. Analyze embedded fonts and object streams—missing fonts or fonts substituted during editing can indicate unauthorized changes. For images, apply forensic techniques that inspect compression signatures and noise patterns; identical noise across separate image regions may show cloning, while abrupt shifts in compression indicate cut-and-paste operations. Employ signature validation tools to check for cryptographic integrity, verifying both the certificate chain and timestamp authorities, and consult certificate revocation lists for any indication of compromised keys.
Integrate automated checks into workflows: set up webhook notifications to receive detailed reports and risk scores when suspicious files are found, and enable human review for borderline cases. For organizations dealing with high volumes, APIs and connectors to cloud storage (Dropbox, Google Drive, S3, OneDrive) streamline ingestion while preserving provenance. When in doubt, cross-reference data with external sources—contact issuing institutions, validate invoice numbers against accounting records, and use the detect fraud in pdf resources for additional automated analysis and reporting options.
Real-world case studies and best practices for organizations
Case study 1: A financial firm received an altered loan agreement where the APR had been modified within embedded text layers. Automated metadata checks revealed that the document's modification timestamp postdated the signing timestamp by several hours. Signature validation showed the digital signature covered an earlier version of the document, indicating later edits. The forensic report highlighted font substitutions in targeted paragraphs, leading the firm to reject the submission and initiate a fraud investigation.
Case study 2: A human resources department was targeted with falsified employment verification PDFs. Image forensics detected duplicated noise patterns across the letterhead, and OCR extracted hidden text blocks inconsistent with the stated issuer. The automated pipeline flagged the differences, generated a transparent report detailing the anomalies, and provided a reproducible hash that matched the file received by HR—evidence that supported legal escalation and prevented a fraudulent hire.
Best practices emerging from these scenarios include establishing strict ingestion protocols, retaining original file hashes, and maintaining audit trails for all document processing activities. Train staff to recognize common signs—mismatched fonts, suspicious metadata, and broken signatures—and combine automated scoring with human review for high-stakes documents. Regularly update signature validation roots and revocation lists, and ensure OCR and image-forensic engines are tuned to the typical document types handled by the organization. Finally, adopt transparent reporting: provide clear, itemized explanations of what was checked, why a risk score was assigned, and actionable next steps to resolve discrepancies. These measures transform detection from an afterthought into an integral part of document security and compliance.
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