Abstract—The increasing prevalence of counterfeit goods in online marketplaces poses a significant threat to brand equity, consumer trust, and overall digital security. Traditional image-based logo verification systems often struggle to accurately capture both visual similarity and spatial consistency, particularly under challenging conditions such as occlusion, manipulation, and severe visual distortions. To address these limitations, this paper proposes a dual-stage deep metric learning and graph-cut framework that integrates semantic logo authentication with detailed forgery segmentation. In the first stage, deep metric learning combined with Adaptive Multi-Center Triplet Loss (AMCTL) is used to map logos into a discriminative embedding space, enabling effective separation between authentic and counterfeit instances. In the second stage, a graph-cut-based optimization technique is employed to localize forged regions through contextual abnormality detection and pairwise energy minimization. The proposed framework is evaluated on both publicly available and customized logo datasets and demonstrates consistent and competitive performance compared to existing approaches such as Deep Semantic Contrastive Learning Classifier (DSCL), Graph-Cut Segmentation (GCut+), and Region-Aware Metric Learning (RAML). Improvements are observed across multiple evaluation metrics, including accuracy, F1-Score, Intersection over Union (IoU), and Area Under the Curve (AUC). Furthermore, the model maintains computational efficiency, making it suitable for practical deployment in real-time e-commerce environments. Overall, the proposed approach provides a scalable and interpretable solution for reliable logo verification in modern online marketplaces.
 
Keywords—logo authentication, deep metric learning, graph-cut segmentation, forgery detection, e-commerce security
 
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