Deep Logo Authenticity: Leveraging R-CNN for Counterfeit Logo Detection in E-commerce

Abstract

In the rapidly evolving realm of electronic commerce, ensuring the accuracy and authenticity of merchandise assumes paramount importance in maintaining consumer trust and platform reliability. One of the prominent challenges encountered within this particular domain revolves around the pervasive prevalence of counterfeit products, often discernible through subtle deviations in brand insignias. This research paper introduces a novel approach to detect counterfeit logos on electronic commerce platforms using Region-based Convolutional Neural Networks (R-CNN). Traditional approaches often rely on manual verification or basic image comparisons, both of which have drawbacks in terms of scalability and consistent accuracy. The methodology utilized in our research capitalizes on the capabilities of deep learning algorithms to precisely identify and classify logos depicted in product images, proficiently distinguishing genuine logos from counterfeit ones with a significant degree of precision. A meticulously curated dataset was compiled, encompassing genuine and counterfeit logos sourced from renowned brands. By means of intensive training, our model demonstrated remarkable aptitude, surpassing the capabilities of contemporary methodologies. The current investigation not only offers a significant contribution to enhancing the security and reliability of electronic commerce platforms, but also establishes the foundation for the advancement of advanced counterfeit detection methodologies within the domain of digital marketplaces.