Counterfeit Product Detection with Deep Learning
In this project, a deep learning approach that detects relevant products for a specific brand and spots counterfeit goods was introduced and developed. With the use of the AllenNLP library and supervised learning, the algorithm processes textual content and performs a categorization that produces a different output in each case. As a result, a trained model that receives six different product features (e commerce platform, search terms, description, price, seller, image tags) and creates predictions for the relevancy or the authenticity of a product was built. The outcome of this work, was a high performance model that achieved 93 accuracy for relevance detection and 83 accuracy for counterfeit detection as a pipeline.