  The field of computer vision has witnessed phenomenal progress in recent
years partially due to the development of deep convolutional neural networks.
However, deep learning models are notoriously sensitive to adversarial examples
which are synthesized by adding quasi-perceptible noises on real images. Some
existing defense methods require to re-train attacked target networks and
augment the train set via known adversarial attacks, which is inefficient and
might be unpromising with unknown attack types. To overcome the above issues,
we propose a portable defense method, online alternate generator, which does
not need to access or modify the parameters of the target networks. The
proposed method works by online synthesizing another image from scratch for an
input image, instead of removing or destroying adversarial noises. To avoid
pretrained parameters exploited by attackers, we alternately update the
generator and the synthesized image at the inference stage. Experimental
results demonstrate that the proposed defensive scheme and method outperforms a
series of state-of-the-art defending models against gray-box adversarial
attacks.
