As the use of Deep Neural Networks (DNNs) becomes pervasive, their vulnerability to adversarial attacks and limitations in handling unseen classes poses significant challenges. The state-of-the-art offers discrete solutions aimed to tackle individual issues covering specific adversarial attack scenarios, classification or evolving learning. However, real-world systems need to be able to detect and recover from a wide range of adversarial attacks without sacrificing classification accuracy and to flexibly act in {\bf unseen} scenarios. In this paper, UNICAD, is proposed as a novel framework that integrates a variety of techniques to provide an adaptive solution. For the targeted image classification, UNICAD achieves accurate image classification, detects unseen classes, and recovers from adversarial attacks using Prototype and Similarity-based DNNs with denoising autoencoders. Our experiments performed on the CIFAR-10 dataset highlight UNICAD's effectiveness in adversarial mitigation and unseen class classification, outperforming traditional models.

翻译：随着深度神经网络（DNN）的广泛应用，其对抗攻击的脆弱性及处理未见类别能力的局限性带来了重大挑战。现有技术提供了离散的解决方案，旨在处理涵盖特定对抗攻击场景、分类或演化学习的个别问题。然而，现实世界的系统需要能够在不牺牲分类准确性的前提下，检测并恢复来自各种对抗攻击，并灵活应对{\bf 未见}场景。本文提出了UNICAD作为一种新颖框架，它整合了多种技术以提供自适应解决方案。针对目标图像分类任务，UNICAD通过结合原型与相似性DNN以及去噪自编码器，实现了精确的图像分类、未见类别的检测以及对抗攻击的恢复。我们在CIFAR-10数据集上进行的实验突显了UNICAD在对抗缓解和未见类别分类方面的有效性，其性能优于传统模型。