Authors : M. Shaik Suzain Saba; Dr. Girish Kumar D.; Sreelakshmi J.

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/2h4trkxx

Scribd : https://tinyurl.com/3sn4wv4b

DOI : https://doi.org/10.38124/ijisrt/26May998

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Accelerated growth in online retail has sharply elevated the volume of fake merchandise circulating across digital markets, creating wide-ranging economic, legal, and safety consequences for both consumers and brand owners. Counterfeit goods are typically engineered to closely mimic genuine products in appearance, making human-led verification unreliable and difficult to scale. Conventional rule-based detection tools have consistently struggled to keep pace with sophisticated and continuously evolving forgery methods. AuthentiCheck is introduced as an AI-powered detection framework that unifies computer vision, NLP, OCR, and metadata validation within a single pipeline. The system jointly analyzes product imagery, listing text, seller profiles, and pricing signals. A cross-modal fusion engine synthesizes evidence from all channels to generate an interpretable authenticity rating and risk classification. Empirical testing confirms this multi-source strategy significantly outperforms single-channel alternatives. The framework prioritizes scalability, interpretability, and ethical deployment.

Keywords : Counterfeit Detection, Computer Vision, Natural Language Processing, Multi-Modal AI, E-Commerce Security

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