Overview

Forgery detection is a foundational component of Beltic’s verification and compliance infrastructure.

As AI-generated and synthetically manipulated documents proliferate, detecting tampering at scale has become critical to maintaining trust, compliance integrity, and onboarding efficiency.

Beltic’s Forgery Detection employs AI-driven forensic models, metadata analysis to determine document authenticity in real time.

It enables Beltic customers to automatically approve legitimate submissions, escalate suspicious cases, and block fraudulent content ensuring security without compromising user experience.

Detection Architecture

Each uploaded document is evaluated through multi-layered validation pipelines.

Every detection layer contributes an independent signal that is aggregated by Beltic’s decision model to produce a unified authenticity verdict.

This modular architecture ensures scalability, explainability, and continuous model evolution.

1. Metadata Integrity Analysis

Analyzes the document’s embedded file-level metadata to verify origin and modification history.

• Extracts creation timestamps, modification logs, and embedded editor identifiers.

• Detects anomalies such as missing creation origins, tampered timestamps, or unexpected software traces.

• Flags documents modified post-issuance — e.g., edited in third-party PDF editors, mobile processors, or photo tools.

Technical Outcome:

High-confidence metadata integrity; anomalies trigger secondary forensic layers.

2. Structural Integrity Analysis

Inspects the internal composition and digital signature structure of the document.

• Parses layout trees, XObject references, and embedded font maps.

• Detects malformed or regenerated object hierarchies inconsistent with legitimate issuers.

• Validates embedded certificates and signature chains.

Technical Outcome:

Authentic, signed documents are automatically approved; structurally inconsistent files are flagged for escalation.

3. Visual and Editing Trace Analysis

Employs computer vision and deep learning classifiers to identify visual artifacts introduced during tampering.

• Recognizes pixel- and layout-level patterns from screenshots, photo edits, or scan reproductions.

• Analyzes compression signatures, noise spectra, and color channel distributions.

• Detects cloning, overlay, and edge blending typical of image-editing tools.

Technical Outcome:

Exposes digitally regenerated or screen-captured documents without penalizing legitimate low-resolution submissions.

4. Image Quality and Source Validation

Assesses the capture environment and image provenance.

• Evaluates resolution, sharpness, and camera signature consistency

• Differentiates between genuine photographs and synthetic, reprojected, or screen-captured inputs.

• Identifies print-scan loops and multi-generation reproductions.

Technical Outcome:

Filters out synthetic and low-integrity images while maintaining high throughput for genuine, low-friction uploads.

5. Synthetic Document Detection

Detects AI-generated or composited documents using generative model forensics and pattern analysis.

• Applies convolutional and transformer-based models to analyze pixel uniformity, texture coherence, and font embedding patterns.

• Cross-references each document’s embedding against Beltic’s internal forgery signature index of known fraudulent templates.

• Flags documents exhibiting LLM/VLM-based generation artifacts or visual token repetition.

Technical Outcome:

Identifies generative forgeries with sub-second latency, enabling real-time fraud prevention.

6. Content and Signature Validation

Verifies the semantic and cryptographic integrity of document content.

• Validates digital certificate chains and cryptographic hashes.

• Detects cross-page inconsistencies (e.g., mismatched identity data or altered fields).

• Confirms structural and textual alignment across document sections.

Technical Outcome:

Authentic, cryptographically verifiable documents are auto-approved, reducing manual review load and onboarding latency.

Verdict and Decisioning Logic

Each detection layer produces discrete confidence scores, which are weighted and combined into a final decision via Beltic’s scoring model.

The resulting verdict determines downstream routing within the orchestration system.

Verdict	Description	System Action
Approve	Authentic and unmodified	Auto-approval and fast-track onboarding
Inconclusive	Partial anomalies or low-confidence signals	Escalate to human review
Deny	Confirmed or high-probability forgery	Immediate block and alert trigger

Technical Note:

All decisions are recorded with traceable signal metadata, allowing full audit reconstruction and explainability for compliance reporting.