Model Extraction of Convolutional Neural Networks with Max-Pooling

Summary

This research studies model extraction of convolutional neural networks that combine ReLU activations and max-pooling layers in the soft-label setting. The authors show that max-pooling critical points capture the difference between two convolutional neurons, and that the local structure of convolution lets them determine the shift between neurons and reconstruct the convolutional kernel. Validated on a compact VGG-style CNN trained on CIFAR-10, the methods permit accurate weight extraction in practice.

Topics

• Adversarial machine learning

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