Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting

@article{Wu2021TowardsAP,
  title={Towards Adversarial Patch Analysis and Certified Defense against Crowd Counting},
  author={Qiming Wu and Zhikang Zou and Pan Zhou and Xiaoqing Ye and Binghui Wang and Ang Li},
  journal={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021}
}
  • Qiming WuZhikang Zou Ang Li
  • Published 22 April 2021
  • Computer Science
  • Proceedings of the 29th ACM International Conference on Multimedia
Crowd counting has drawn much attention due to its importance in safety-critical surveillance systems. Especially, deep neural network (DNN) methods have significantly reduced estimation errors for crowd counting missions. Recent studies have demonstrated that DNNs are vulnerable to adversarial attacks, i.e., normal images with human-imperceptible perturbations could mislead DNNs to make false predictions. In this work, we propose a robust attack strategy called Adversarial Patch Attack with… 

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