The proper handling of out-of-distribution (OOD) samples in deep classifiers
is a critical concern for ensuring the suitability of deep neural networks in
safety-critical systems. Existing approaches developed for robust OOD detection
in the presence of adversarial attacks lose their performance by increasing the
perturbation levels. This study proposes a method for robust classification in
the presence of OOD samples and adversarial attacks with high perturbation
levels. The proposed approach utilizes a fully connected neural network that is
trained to use training samples as its attractors, enhancing its robustness.
This network has the ability to classify inputs and identify OOD samples as
well. To evaluate this method, the network is trained on the MNIST dataset, and
its performance is tested on adversarial examples. The results indicate that
the network maintains its performance even when classifying adversarial
examples, achieving 87.13% accuracy when dealing with highly perturbed MNIST
test data. Furthermore, by using fashion-MNIST and CIFAR-10-bw as OOD samples,
the network can distinguish these samples from MNIST samples with an accuracy
of 98.84% and 99.28%, respectively. In the presence of severe adversarial
attacks, these measures decrease slightly to 98.48% and 98.88%, indicating the
robustness of the proposed method.

在面对高干扰水平的离群样本和对抗性攻击时，通过利用训练样本作为吸引子训练全连接神经网络，可以提高网络的稳健性和对离群样本的识别能力，从而实现稳健分类，证实其在高度干扰的 MNIST 测试数据上达到 87.13% 的准确率，并以 98.84% 和 99.28% 的准确率分别区分 fashion-MNIST 和 CIFAR-10-bw 作为离群样本。即便在面对严重的对抗性攻击时，这些度量略有降低至 98.48% 和 98.88%，从而证明所提方法的稳健性。