• Corpus ID: 238857225

DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks

@article{Wang2021DIAAAI,
  title={DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks},
  author={Yixiang Wang and Jiqiang Liu and Xiaolin Chang and Jianhua Wang and Ricardo J. Rodr'iguez},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.07305}
}
White-box Adversarial Example (AE) attacks towards Deep Neural Networks (DNNs) have a more powerful destructive capacity than black-box AE attacks in the fields of AE strategies. However, almost all the white-box approaches lack interpretation from the point of view of DNNs. That is, adversaries did not investigate the attacks from the perspective of interpretable features, and few of these approaches considered what features the DNN actually learns. In this paper, we propose an interpretable… 

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References

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