UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification
<|reference_start|>UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification: 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.<|reference_end|>
@article{pellicer2024unicad:,
    title={UNICAD: A Unified Approach for Attack Detection, Noise Reduction and
  Novel Class Identification},
    author={Alvaro Lopez Pellicer, Kittipos Giatgong, Yi Li, Neeraj Suri, Plamen
  Angelov},
    journal={arXiv preprint arXiv:2406.16501},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16501},
    primaryClass={cs.CV cs.AI cs.LG}
}