Leveraging YOLOv8 in Orifake: A deep learning system for combating counterfeit logos

Muhammad Faliq Haziq Mohd Saipullizan; Rashidah Mokhtar; Shamimi A. Halim

doi:10.21834/e-bpj.v10iSI31.6929

Authors

Muhammad Faliq Haziq Mohd Saipullizan Computing Science Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Shah Alam, 40450 Malaysia
Rashidah Mokhtar Computing Science Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Cawangan Johor, 85000 Malaysia
Shamimi A. Halim Computing Science Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Shah Alam, 40450 Malaysia,

DOI:

https://doi.org/10.21834/e-bpj.v10iSI31.6929

Keywords:

Deep Learning, Logo Detection, YOLOv8, Brand Authentication

Abstract

The proliferation of counterfeit logos necessitates efficient methods for brand protection. This study addresses this challenge by introducing Orifake, a deep-learning model leveraging YOLOv8 (You Only Look Once) for logo classification. The model analyses logos (Adidas, Nike, Puma) for subtle features like colour, patterns, shapes, and textures. The evaluation demonstrates an average recall of 78.5%. Notably, Orifake excels at identifying fake NIKE logos (recall: 94.9%). However, further refinement is needed for original NIKE logos due to their lower mAP-95 value. This research highlights the potential of Artificial Intelligence (AI) logo detection for brand protection against increasing online fraud.

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