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 automated PDF verification works: Upload, analyze, and explain

Modern PDF verification systems follow a defined pipeline that converts a file into analyzable components and then applies a blend of rule-based checks and machine learning models. After the initial upload, the system extracts the file's raw bytes, embedded objects, fonts, images, and metadata. Extraction is critical because many forgeries hide telltale signs not visible in normal viewers: timestamps with improbable sequencing, mismatched creation and modification records, or embedded code that alters appearance. Once the contents are parsed, the next step is structural analysis. This includes examining document object streams, XMP metadata blocks, incremental update histories, and cross-reference tables. Structural anomalies often reveal tampering—such as deleted revision records or stream edits that leave inconsistencies in the file’s internal tree.

Concurrently, text-focused analysis evaluates the logical flow and typographic consistency. Natural language models and layout classifiers identify suspect text insertions, font substitution, and alignment changes that indicate manual edits. Image forensics then inspects raster content: checking for cloned regions, compression artifacts, and meta-information embedded in images (EXIF, embedded ICC profiles). Digital signature verification is another pillar. Valid signatures should match cryptographic standards and certificate chains; signature fields that lack corresponding certificate authorities or that have been applied using unverifiable keys raise immediate red flags. Finally, an explainable output synthesizes findings into a transparency-focused report: which metadata fields were abnormal, which pages contained edits, and what confidence level the AI assigns to each suspicion. This combined approach allows users to move from a simple suspect feeling to an actionable, evidence-backed assessment of whether a document is authentic or manipulated.

Key indicators of a fake PDF and how to inspect them manually

Learning the core indicators that distinguish legitimate PDFs from forgeries empowers users to perform quick manual checks before relying on automation. Start with metadata: open the document properties and compare the creation date, modification date, author fields, and PDF producer. Discrepancies like a creation date that postdates claimed issuance or a producer field indicating an unexpected editor (for example, a consumer PDF editor when a bank letter should be generated by a secure system) are suspicious. Next, examine the fonts and text flow. If sections show different font families or the spacing and kerning change abruptly, these can be signs of copy-paste edits. Text that looks visually consistent but fails text-selection tests (e.g., selecting characters returns gibberish) may be the result of rasterized content masquerading as searchable text.

Embedded images and scanned pages deserve special scrutiny. Use zoom and contrast adjustment to look for cloned regions, blur patterns, or inconsistent shadowing that betray cut-and-paste operations. Check annotations and form fields: hidden form fields can alter visible content or overlay new text at render time. For signatures, verify cryptographic details rather than trusting visual cursive alone—valid signatures will include certificate details and a signature timestamp tied to a trusted authority. Also, beware of layered content; PDFs support multiple overlapping objects, and a forgery may hide an original signature layer beneath an overlay. If available, compare the suspect document against known originals: a byte-level comparison or visual diff will reveal subtle edits. When manual checks are inconclusive, automated tools provide in-depth forensic analysis. For a one-stop technical check, use a specialized service to detect fake pdf and obtain an evidence-driven report showing which indicators triggered concern and why.

Real-world examples, case studies, and practical defenses

Real-world incidents highlight how simple manipulations can have serious consequences. In a common scenario, an altered invoice changes payee details and bank account numbers; visual inspection may miss the edit if fonts and spacing are mocked up carefully. For example, an organization once received an invoice where the bank routing digits were replaced by visually similar characters from another font set; the invoice appeared legitimate in previews but failed a byte-level font consistency check. Another frequent case involves academic credentials: diplomas are scanned and minor date or name edits are applied to create counterfeit certificates. Forensic analysis of image compression and XMP metadata often exposes the original scanning device and subsequent editing tools, which differ from legitimate issuer profiles.

Practical defenses combine policy, training, and technology. Implement strict document intake procedures: require original-sent channels, verify digital signatures against known certificate authorities, and compare documents to a canonical repository. Train staff to recognize red flags like mismatched metadata, suspicious fonts, or out-of-sequence timestamps. Technically, deploy automated checks in the workflow: on upload, parse metadata, run layout and image-forensics scans, and validate signatures. Set thresholds for manual review and create an audit trail that records the verification steps and results. When encountering a suspect document, preserve the original file and export diagnostic artifacts (metadata dump, hash, and comparison images) to support disputes or legal proceedings. Case studies show that organizations that combine these defenses reduce fraud impact substantially, cutting successful forgeries by detecting early inconsistencies and preventing fraudulent transactions or approvals.