  Since 2014, artificial neural networks have been known to be vulnerable to
adversarial attacks, which can fool the network into producing wrong or
nonsensical outputs by making humanly imperceptible alterations to inputs.
While defenses against adversarial attacks have been proposed, they usually
involve retraining a new neural network from scratch, a costly task. In this
work, I introduce the Finite Gaussian Neuron (FGN), a novel neuron architecture
for artificial neural networks. My works aims to: - easily convert existing
models to Finite Gaussian Neuron architecture, - while preserving the existing
model's behavior on real data, - and offering resistance against adversarial
attacks. I show that converted and retrained Finite Gaussian Neural Networks
(FGNN) always have lower confidence (i.e., are not overconfident) in their
predictions over randomized and Fast Gradient Sign Method adversarial images
when compared to classical neural networks, while maintaining high accuracy and
confidence over real MNIST images. To further validate the capacity of Finite
Gaussian Neurons to protect from adversarial attacks, I compare the behavior of
FGNs to that of Bayesian Neural Networks against both randomized and
adversarial images, and show how the behavior of the two architectures differs.
Finally I show some limitations of the FGN models by testing them on the more
complex SPEECHCOMMANDS task, against the stronger Carlini-Wagner and Projected
Gradient Descent adversarial attacks.
