  Many existing deep learning models are vulnerable to adversarial examples
that are imperceptible to humans. To address this issue, various methods have
been proposed to design network architectures that are robust to one particular
type of adversarial attacks. It is practically impossible, however, to predict
beforehand which type of attacks a machine learn model may suffer from. To
address this challenge, we propose to search for deep neural architectures that
are robust to five types of well-known adversarial attacks using a
multi-objective evolutionary algorithm. To reduce the computational cost, a
normalized error rate of a randomly chosen attack is calculated as the
robustness for each newly generated neural architecture at each generation. All
non-dominated network architectures obtained by the proposed method are then
fully trained against randomly chosen adversarial attacks and tested on two
widely used datasets. Our experimental results demonstrate the superiority of
optimized neural architectures found by the proposed approach over
state-of-the-art networks that are widely used in the literature in terms of
the classification accuracy under different adversarial attacks.
