Anti-Counterfeit Detection System
Visual inspection R&D for distinguishing genuine and counterfeit packaging using learned features, OCR, and classical image-quality checks.
Internal R&D / Personal Engineering Project at Bondstein Technologies Ltd.
Workflow at a glance
Provide packaging images
Capture visible product and label regions.
Extract visual cues
Combine learned features and OCR.
Compare evidence
Assess candidate packaging differences.
Human review
Review R&D results; authenticity is not assured.
Project facts & reported results
Implementation fact
Implementation fact
Project Overview
Problem Statement
Packaging differences can be subtle and vary with camera, lighting, print quality, and product type, requiring controlled capture and reviewable evidence.
Approach & Methodology
Explored detection, learned visual features, OCR, and classical image checks for comparing packaging appearance. Candidate differences are surfaced for review rather than treated as definitive proof of authenticity.
Outcome & Scope
Built a configurable inspection workflow for controlled evaluation and operator review; production performance must be validated per product and capture setup.
Technical Challenges & Solutions
Legitimate packaging variation
Genuine products can vary in print, lighting, or packaging revisions.
Compare multiple visual cues and retain a manual review step.
Limits of visual evidence
Appearance alone cannot establish a supply-chain authenticity claim.
Frame outputs as inspection cues and keep production validation separate.
System Architecture
Visual packaging comparison for internal R&D.
Key Components:
Prepare packaging images for inspection.
Combine learned features and OCR cues.
Identify candidate visual differences.
Present evidence and record inspection observations.
Key Features & Capabilities
Packaging-region analysis
Learned visual-feature comparison
OCR and classical image checks
Candidate differences for manual assessment
Current Scope & Limitations
Internal R&D remains in development; no production adoption or measured accuracy is claimed.
Visual similarity or difference alone does not prove authenticity.