  Despite its great success, deep learning severely suffers from robustness;
that is, deep neural networks are very vulnerable to adversarial attacks, even
the simplest ones. Inspired by recent advances in brain science, we propose the
Denoised Internal Models (DIM), a novel generative autoencoder-based model to
tackle this challenge. Simulating the pipeline in the human brain for visual
signal processing, DIM adopts a two-stage approach. In the first stage, DIM
uses a denoiser to reduce the noise and the dimensions of inputs, reflecting
the information pre-processing in the thalamus. Inspired from the sparse coding
of memory-related traces in the primary visual cortex, the second stage
produces a set of internal models, one for each category. We evaluate DIM over
42 adversarial attacks, showing that DIM effectively defenses against all the
attacks and outperforms the SOTA on the overall robustness.
