  Neural network quantization has become increasingly popular due to efficient
memory consumption and faster computation resulting from bitwise operations on
the quantized networks. Even though they exhibit excellent generalization
capabilities, their robustness properties are not well-understood. In this
work, we systematically study the robustness of quantized networks against
gradient based adversarial attacks and demonstrate that these quantized models
suffer from gradient vanishing issues and show a fake sense of robustness. By
attributing gradient vanishing to poor forward-backward signal propagation in
the trained network, we introduce a simple temperature scaling approach to
mitigate this issue while preserving the decision boundary. Despite being a
simple modification to existing gradient based adversarial attacks, experiments
on multiple image classification datasets with multiple network architectures
demonstrate that our temperature scaled attacks obtain near-perfect success
rate on quantized networks while outperforming original attacks on
adversarially trained models as well as floating-point networks. Code is
available at https://github.com/kartikgupta-at-anu/attack-bnn.
