Presentation: Detecting Similar Id Documents Using Deep Learning

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 2:20pm - 2:30pm

Day of week: Wednesday

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Abstract

Identity verification is the process in which we digitally confirm the legitimacy of a real world document. This process is necessary for many businesses to meet their compliance requirements and mitigate their fraud risk. A common form of fraud is the duplication and alteration of stolen documents across multiple user accounts. In this talk, we will discuss how Coinbase solves the problem of detecting similar identity documents using deep learning.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Burkay Gur

Risk Engineer @Coinbase

Find Burkay Gur at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.