  In recent years, deep learning has shown impressive performance on many
tasks. However, recent researches showed that deep learning systems are
vulnerable to small, specially crafted perturbations that are imperceptible to
humans. Images with such perturbations are the so called adversarial examples,
which have proven to be an indisputable threat to the DNN based applications.
The lack of better understanding of the DNNs has prevented the development of
efficient defenses against adversarial examples. In this paper, we propose a
two-stream architecture to protect CNN from attacking by adversarial examples.
Our model draws on the idea of "two-stream" which commonly used in the security
field, and successfully defends different kinds of attack methods by the
differences of "high-resolution" and "low-resolution" networks in feature
extraction. We provide a reasonable interpretation on why our two-stream
architecture is difficult to defeat, and show experimentally that our method is
hard to defeat with state-of-the-art attacks. We demonstrate that our
two-stream architecture is robust to adversarial examples built by currently
known attacking algorithms.
