Materials for ECCV-2020 Paper #2274.

• Title: Adversarial Ranking Attack and Defense
• Authors: Mo Zhou, Zhenxing Niu, Le Wang, Qilin Zhang, Gang Hua
• Preprint: https://arxiv.org/abs/2002.11293
• Full-Preprint: 2002.11293v3-full.pdf (with more graphs)
• Video: {Bilibili(English)} {Bilibili(Chinese)} {Youtube(English)}
• Slides: (One-page version) (long version)
• Code: available here (Released on Aug 24)

Definition of Adversarial ranking attack: adversarial ranking attack aims raise or lower the ranks of some chosen candidates C={c₁,c₂, ... ,cₘ} with respect to a specific query set Q={q₁,q₂, ... ,qw}. This can be achieved by either Candidate Attack (CA) or Query Attack (QA).

1. The adversarial ranking attack is defined and implemented, which can intentionally change the ranking results by perturbing the candidates or queries.

2. An adversarial ranking defense method is proposed to improve the ranking model robustness, and mitigate all the proposed attacks simultaneously.

The paper (PDF file) is distributed under the CC BY-SA-NC 4.0 License.

The code is published under the Apache-2.0 License.

Bibtex for the ECCV version:

@InProceedings{advrank,
  author={Zhou, Mo and Niu, Zhenxing and Wang, Le and Zhang, Qilin and Hua, Gang},
  title={Adversarial Ranking Attack and Defense},
  booktitle={ECCV 2020},
  year={2020},
  pages={781--799},
  isbn={978-3-030-58568-6}
}

Bibtex for the ArXiv preprint version:

@article{zhou2020advrank,
  title={Adversarial Ranking Attack and Defense},
  author={Zhou, Mo and Niu, Zhenxing and Wang, Le and Zhang, Qilin and Hua, Gang},
  journal={arXiv preprint arXiv:2002.11293},
  year={2020}
}

1. A. Madry et.al. Towards Deep Learning Models Resistant to Adversarial Attacks