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 to Quickly Upload and Scan a PDF for Authenticity
A reliable verification workflow starts with a frictionless upload and scanning process. Modern document-checking platforms allow users to drag and drop files, choose files manually, or connect to cloud storage services such as Dropbox, Google Drive, Amazon S3, and Microsoft OneDrive. For high-volume or automated environments, an API integration enables seamless routing of documents into a processing pipeline so every incoming PDF is checked without manual steps. The upload stage should capture both the original file and any accompanying images or attachments to preserve the file's integrity during analysis.
Once uploaded, the system executes a multi-layered scan that includes quick heuristics and deeper forensic checks. Quick heuristics identify obvious red flags: unusual file size, unexpected file extension changes (for example, a .pdf wrapper around a different container), and missing or empty metadata fields. Deeper checks parse the PDF at the object level, examining embedded streams, fonts, and image content. If the file contains scanned pages, optical character recognition (OCR) runs to convert images to searchable text and compare it against expected content patterns.
Time-to-result matters: instant feedback helps stop fraud before it spreads. Many platforms provide results in seconds and offer a clear breakdown of what was examined. A good dashboard highlights suspicious elements, pinpoints pages where anomalies were detected, and supplies raw artifacts such as extracted metadata and logs. For developers and enterprise teams, webhook notifications and API callbacks ensure the verification outcome feeds into downstream workflows—flagging invoices, contracts, or legal documents for manual review when needed. The combination of easy upload, immediate scanning, and actionable outputs is essential to reduce risk and speed response.
Technical Signals: Metadata, Text Structure, and Embedded Signatures
Detecting a fake PDF depends on interpreting several technical signals that collectively reveal tampering. Metadata is the digital fingerprint of a document: creation and modification timestamps, author and producer fields, software stamp, and PDF version. Inconsistencies—such as a creation date later than a supposed signing date, or a mismatch between the declared producer and the embedded font set—are strong indicators of manipulation. Metadata can also hide traces of sanitization when malicious actors overwrite fields to erase a history of edits.
Text structure offers another layer of evidence. PDFs built from native text maintain logical structure, selectable text, and consistent font encoding. When pages are scanned and saved as images, the text becomes an embedded raster or a set of OCR results. Discrepancies between selectable text and visible text, unusual kerning, or mismatched font families across similar sections suggest cut-and-paste edits or composition from multiple sources. Pattern analysis can also detect repeated paragraphs with different metadata, signaling copy-paste attacks or template misuse.
Embedded signatures and cryptographic seals are critical for high-stakes documents. A valid digital signature includes a timestamp, signer certificate chain, and a cryptographic digest; verification should confirm the signature is intact and the certificate is trusted. Visual signatures (scanned handwritten signatures) require separate forensic checks: inconsistencies in pen stroke continuity, imprecise alignment, or signs of layering indicate visual forgery. Additional artifacts—such as hidden form fields, incremental updates layered in the PDF's object structure, or manipulated XMP blocks—further expose attempts to conceal edits. Combining these signals into a cohesive risk score enables objective judgment about a document's authenticity.
Interpreting the Report: What Results Mean and Real-World Examples
A comprehensive verification report translates low-level findings into actionable conclusions. Each report typically lists checks performed—metadata audit, signature validation, content integrity, OCR comparison, and image forensics—alongside a risk rating and an explanation for every flagged item. For example, a report might show a mismatched creation date and signature timestamp and explain why this undermines trust. Transparency is vital: users should see the raw data (extracted metadata, certificate chain details, or image diffs) plus a plain-language rationale for the verdict so non-technical stakeholders can act confidently.
Real-world examples help clarify how to use these reports. Consider a forged invoice: image analysis may reveal cloned logos, inconsistent tax numbers, and pixel-level seams where fields were altered. The report can point to the exact page and region, include a visual overlay showing edits, and recommend rejecting payment. In another case, a modified academic transcript may present normal-looking text but fail cryptographic signature checks; a valid institution signature absent or broken is often decisive. For legal documents, incremental updates in the PDF object stream can show that additional pages or clauses were appended after signing—critical evidence in disputes.
Case studies also illustrate operational integration. One procurement team reduced payment fraud by routing every vendor invoice through automated verification with webhook alerts for any high-risk score. A human resources department combined OCR validation and name-matching rules to flag altered employment records before onboarding. For on-demand needs, a single click to detect fake pdf can surface immediate issues, enabling rapid escalation. Detailed reports, combined with contextual controls like retention of originals and audit trails, form the backbone of a defensible document verification strategy without compromising daily workflows.
