feat(fraud): AI fraud detection engine for color and duplicate scans (#9)#168
feat(fraud): AI fraud detection engine for color and duplicate scans (#9)#168saidai-bhuvanesh wants to merge 10 commits into
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🔗 Upstream Issue Connection
Closes #167
This Pull Request is officially linked to and resolves Issue #167 (Feature 9: AI Fraud Detection for Scans & Images) in the upstream repository.
Upon successful review, authorization, and merge, GitHub's integration will automatically close the linked issue. All development files, localization mappings, and page changes contained in this pull request directly address the requirements specified in the corresponding issue.
What changes are made?
backend/main.py): Implemented color variance check on the backend. Highly disproportionate gill saturation (>90%) on deteriorated fish bodies (<55%) is flagged as artificial gill coloring fraud.src/pages/AnalysisDashboard.tsx): Designed a flashing warning block at the top of the dashboard displaying warning details if any fraud indicators are flagged.Technical Depth and Verification
Dyeing gills red is a common fraud practice in local fish markets to hide spoilage. By comparing gill color scores to body freshness index values, we can detect anomalous disparities programmatically. The UI warning banner immediately flags these scans, notifying consumers.
Tested by uploading a deteriorated fish scan with artificially inflated gill scores. The dashboard successfully renders the suspected fraud banner with details on the detected anomaly.