  Open-set recognition and adversarial defense study two key aspects of deep
learning that are vital for real-world deployment. The objective of open-set
recognition is to identify samples from open-set classes during testing, while
adversarial defense aims to robustify the network against images perturbed by
imperceptible adversarial noise. This paper demonstrates that open-set
recognition systems are vulnerable to adversarial samples. Furthermore, this
paper shows that adversarial defense mechanisms trained on known classes are
unable to generalize well to open-set samples. Motivated by these observations,
we emphasize the necessity of an Open-Set Adversarial Defense (OSAD) mechanism.
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual
Learning (OSDN-CAML) as a solution to the OSAD problem. The proposed network
designs an encoder with dual-attentive feature-denoising layers coupled with a
classifier to learn a noise-free latent feature representation, which
adaptively removes adversarial noise guided by channel and spatial-wise
attentive filters. Several techniques are exploited to learn a noise-free and
informative latent feature space with the aim of improving the performance of
adversarial defense and open-set recognition. First, we incorporate a decoder
to ensure that clean images can be well reconstructed from the obtained latent
features. Then, self-supervision is used to ensure that the latent features are
informative enough to carry out an auxiliary task. Finally, to exploit more
complementary knowledge from clean image classification to facilitate feature
denoising and search for a more generalized local minimum for open-set
recognition, we further propose clean-adversarial mutual learning, where a peer
network (classifying clean images) is further introduced to mutually learn with
the classifier (classifying adversarial images).
