Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks

Understand

Although deep neural networks (DNNs) have achieved great success in many tasks, they can often be fooled by \emph{adversarial examples} that are generated by adding small but purposeful distortions to natural examples.

• Previous studies to defend against adversarial examples mostly focused on refining the DNN models, but have either shown limited success or required expensive computation.
• We propose a new strategy, \emph{feature squeezing}, that can be used to harden DNN models by detecting adversarial examples.
• Feature squeezing reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample.