Stolen credit cards are a major problem faced by many companies, including Uber. Karthik Ramasamy and Lenny Evans detail a new weapon against stolen credit cards that uses computer vision to scan credit cards, verifying possession of the physical card with basic fake card detection capabilities. There are few labeled datasets of credit cards, so Uber uses transfer learning on public datasets to generate synthetic data to train its models.
OCR is a well-studied problem, and there are many approaches to building an OCR framework. Karthik and Lenny discuss the benefits and trade-offs of various approaches as well as challenges they faced with their model, including running it on mobile devices, handling various lighting and background conditions, and making the model-size footprint small. If you want to incorporate object and text detection models running on-device into your platform, this is the talk for you.
Karthik is a machine learning engineer leading the data science experience for Kubeflow and working primarily on Kubeflow Fairing project . Before Google, Karthik was part of ML platform team at Uber self driving team and before that led a team of data scientists in Uber’s risk team. He is passionate about making data scientists productive.
Lenny Evans is a data scientist at Uber focused on the applications of unsupervised methods and deep learning to fraud prevention, specifically developing anomaly detection models to prevent account takeovers and computer vision models for verifying possession of credit cards.
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