AI‑Enabled Product Integrity Management: Computer Vision for Authenticity and Condition Assessment

• Muneeb Uddin Syed

  Author

Ensuring product authenticity and in‑market quality has become a core responsibility for product and operations teams, especially in categories exposed to counterfeiting and spoilage. This paper presents an AI‑driven framework that uses computer vision and machine learning to support product integrity management by automating logo authenticity checks and physical condition assessment. A multimodel pipeline combining object detection, convolutional neural networks and classical classifiers is applied to branded items and perishable goods, enabling near real‑time detection of counterfeit logos and early signs of quality degradation from standard product images. Experimental results show high accuracy in distinguishing genuine from fraudulent products as well as in classifying fresh versus spoiled items, demonstrating the system’s potential as a decision‑support tool for product managers, quality teams and supply‑chain partners. By embedding these AI capabilities into product workflows, brands can reduce counterfeit circulation, protect customer trust, and make more data‑driven decisions on inventory, recalls and lifecycle interventions.

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