VERIINTERN AI INTELLIGENT FAKE DETECTION SYSTEM FOR INTERNSHIP OFFERS

This Paper Presented Veriintern Ai, A Multi-Layer Fusion Framework For Detecting Fraudulent Internship Offers That Combines Linguistic Pattern Analysis, Homoglyph-Aware Identity Verification, Url And Domain Risk Assessment, And Real-Time Web Intelligence Into A Single Weighted Decision Engine With Explicit Override Logic. The System’s Central Contribution Is Its Resolution Of The Identity–Intent Contradiction Problem, In Which A Scam Offer Borrows The Unaltered Name Of A Genuine Organization While Simultaneously Demanding An Illegitimate Payment — A Case That Defeats Detection Approaches Relying On Identity Verification Or Keyword Matching In Isolation. By Fusing Four Independent Analytical Signals And Applying Targeted Override Rules For High-Confidence Edge Cases, The System Aims To Reliably Flag Both Visual Impersonation Attacks And Contradiction-Based Scams While Minimizing False Positives Against Legitimate Recruitment Language.   Future Work Includes Expanding The Verified-Company Database Through Automated Ingestion From Corporate Registries, Incorporating Additional Knowledge-Base Sources Beyond Wikipedia To Improve Coverage Of Regional And Smaller Organizations, Conducting A Quantitative Evaluation Against A Labeled Corpus Of Real And Fraudulent Internship Offers To Establish Formal Precision And Recall Metrics, And Extending The System As A Browser Extension Or Mobile Application To Provide Real-Time Protection At The Point Where Students First Encounter Internship Solicitations.

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