  Adversarial robustness poses a critical challenge in the deployment of deep
learning models for real-world applications. Traditional approaches to
adversarial training and supervised detection rely on prior knowledge of attack
types and access to labeled training data, which is often impractical. Existing
unsupervised adversarial detection methods identify whether the target model
works properly, but they suffer from bad accuracies owing to the use of common
cross-entropy training loss, which relies on unnecessary features and
strengthens adversarial attacks. We propose new training losses to reduce
useless features and the corresponding detection method without prior knowledge
of adversarial attacks. The detection rate (true positive rate) against all
given white-box attacks is above 93.9% except for attacks without limits
(DF($\infty$)), while the false positive rate is barely 2.5%. The proposed
method works well in all tested attack types and the false positive rates are
even better than the methods good at certain types.
