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|Session Name:||ML Tutorial Day: Beating Wallhacks using Deep Learning with Limited Resources|
|Company Name(s):||Nexon Korea|
|Track / Format:||Programming|
|Overview:||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.|