Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Understand

Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge.

• All efforts to harden classifiers against such attacks have seen limited success.
• Two distinct categories of samples to which deep networks are vulnerable, "adversarial samples" and "fooling samples", have been tackled separately so far due to the difficulty posed when considered together.
• In this work, we show how one can address them both under one unified framework.