Towards unlocking the mystery of adversarial fragility of neural networks
<|reference_start|>Towards unlocking the mystery of adversarial fragility of neural networks: In this paper, we study the adversarial robustness of deep neural networks for classification tasks. We look at the smallest magnitude of possible additive perturbations that can change the output of a classification algorithm. We provide a matrix-theoretic explanation of the adversarial fragility of deep neural network for classification. In particular, our theoretical results show that neural network's adversarial robustness can degrade as the input dimension $d$ increases. Analytically we show that neural networks' adversarial robustness can be only $1/\sqrt{d}$ of the best possible adversarial robustness. Our matrix-theoretic explanation is consistent with an earlier information-theoretic feature-compression-based explanation for the adversarial fragility of neural networks.<|reference_end|>
@article{gao2024towards,
    title={Towards unlocking the mystery of adversarial fragility of neural
  networks},
    author={Jingchao Gao, Raghu Mudumbai, Xiaodong Wu, Jirong Yi, Catherine Xu,
  Hui Xie, Weiyu Xu},
    journal={arXiv preprint arXiv:2406.16200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.16200},
    primaryClass={cs.LG cs.CR cs.IT eess.SP math.IT}
}