An Experimental Study of Automatic Detection and Measurement of Counterfeit in Brand Search Results

Abstract

Brand search results are poisoned by fake ecommerce websites that infringe on the trademark rights of legitimate holders. In this article, we study how to tackle and measure this problem automatically. We present a pipeline with two machine learning stages that can detect the ecommerce websites present in the list of brand search results and distinguish between legitimate and fake ecommerce websites. For each classification task, we identify and extract suitable learning features and study their relative importance. Through a prototype system termed RI.SI.CO., we show that this approach is feasible, fast, and more accurate than both existing systems for trustworthiness assessment and non-expert humans. We next introduce two complementary metrics for evaluating the counterfeit incidence in brand search results: namely, a chart-based and a single-value measure. They allow us to analyze and compare counterfeit at various levels, including single brands within a specific sector as well as whole sectors. Experimenting with two luxury goods sectors, we report a number of interesting findings about how the main search parameters (e.g., search engine, query type, number of search results seen) affect counterfeiting and how this activity changes with time. On the whole, our research offers new insights and some very practical and useful means of analyzing and measuring counterfeit in brand search results, thus increasing awareness of and knowledge about this phenomenon and enabling targeted anti-counterfeiting actions.