GDC is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them. Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

GDC + VRDC 2019 Session Scheduler

View, browse and sort the ever-growing list of GDC sessions by pass type, track, format, and more! With Session Scheduler, create an account to build your own GDC schedule and access it anywhere, including on the GDC app, once live.

If you've registered for GDC, you can use the same login credentials. Adding a session to your schedule does not guarantee you a seat. Sessions do fill up, so please arrive early to sessions that you would like to attend.

ML Tutorial Day: Beating Wallhacks using Deep Learning with Limited Resources

Junsik Hwang  (Machine Learning Engineer, Nexon Korea)

Location: Room 303, South Hall

Date: Tuesday, March 19

Time: 10:00 am - 11:00 am

Pass Type: All Access, GDC Conference + Summits, GDC Summits - Get your pass now!

Topic: Programming

Format: Tutorial

Tutorials: ML Tutorial Day

Vault Recording: Video

Audience Level: All

Albeit having compelling performance, deep learning requires an extensive database and massive computing power, and therefore considerable investment. In this session, Junsik will present how Nexon Korea has developed a real-time automated wallhack detection system using Convolutional Neural Networks with a small dataset and a single GPU. By using Class Activation Maps, the network finds suspicious areas within a screenshot that improves the credibility of the model's performance and makes debugging datasets much more efficient. Model Interpretability plays a crucial role in incorporating deep learning with the existing abuser control policies. As a result, the system now detects abusers in real-time and reduces manual inspection labor significantly.

Takeaway

Attendees will walk away from this session with practical tips on how to build a wallhack detection system using deep learning for their services even with a limited dataset and trust.

Intended Audience

This session is intended for anyone who is interested in deep learning and its practical usage.