Design of a Modular Framework for Noisy Logo Classification in Fraud Detection.

In this paper, we introduce a modular framework to detect noisy logo appearing on online merchandise images so as to support the forensics investigation and detection of increasing online counterfeit product trading and fraud cases. The proposed framework and system is able to perform an automatic logo image classification on realistic and noisy product images. The novel contributions in this work include the design of a modular SVM-based logo classification framework, and its internal segmentation module, two new feature extractions modules, and the decision algorithm for noisy logo detection. We developed the system to perform an automated multi-class product images classification, which achieves promising results on logo classification experiments of Louis Vuitton, Chanel and Polo Ralph Lauren.