International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The proliferation of counterfeit merchandise in the global retail market represents a critical economic challenge, undermining brand integrity and exposing consumers to inferior, unregulated products. Traditional methods of authentication—often reliant on manual inspection by human experts—suffer from a "scalability-accuracy" bottleneck, rendering them inefficient for high-volume supply chains and e-commerce platforms. Furthermore, conventional image classification models frequently struggle with the nuanced, localized distortions typical of high-quality "super-fakes," such as incorrect font kerning or minor geometric deviations. This research introduces "Logo LIES" (Logo Identification & Estimation System), an integrated forensic framework that unifies real-time object detection with an automated verification pipeline. The system bypasses the limitations of standard two-stage detectors by adopting the Ultralytics YOLOv8 architecture, a single-shot regression model optimized for speed and precision. By utilizing a custom-trained dataset of authentic and counterfeit brand marks, the framework achieves high-fidelity localization of logo anomalies in sub-second inference times. To resolve the challenge of user accessibility, the system implements a "Liquid" User Interface coupled with an AI-driven forensic chatbot powered by the Google Gemini API. This architecture is specifically engineered to abstract complex neural network outputs into actionable, plain-language advice. A defining innovation of this project is its dual-modality inference engine, which facilitates both live webcam scanning and static high-resolution image analysis through a shared Flask middleware. Empirical testing confirms that the proposed system delivers a robust, low-latency solution capable of operating on standard consumer hardware without dedicated GPU acceleration. By democratizing access to advanced brand protection tools, this work contributes to the development of a secure digital retail ecosystem.

Downloads: | DOI: 10.17148/IJARCCE.2026.15176