Overview

Recent deep-learning-based methods achieve great performance on various vision applications. However, insufficient robustness on adversarial cases limits real-world applications of deep-learning-based methods. AROW workshop aims to explore adversarial examples, as well as, evaluate and improve the adversarial robustness of computer vision systems.

Topics of AROW workshop include but are not limited to:

  • Improving model robustness against unrestricted adversarial attacks
  • Improving generalization to out-of-distribution samples or unforeseen adversaries
  • Discovery of real-world adversarial examples
  • Novel architectures with robustness to occlusion, viewpoint, and other real-world domain shifts
  • Domain adaptation techniques for robust vision in the real world
  • Datasets for evaluating model robustness
  • Structured deep models and explainable AI

  • Prize

    The workshop is sponsored by the Future Fund regranting program. The funding covers three Best Paper Awards ($10,000 each, $30,000 in total). The awarded papers should study model robustness to threats beyond small l_p perturbations (e.g., adversarially optimized fog and snow effects, adversarial patches, adversarial elastic distortions, new attacks, etc.). The best papers will research attacks with large budgets that are perceptible, and/or attacks with specifications that are not known beforehand and are unforeseen.


    Submission

    Submission format:

    Submissions need to be anonymized and follow the ECCV 2022 Author Instructions. The workshop considers two types of submissions: (1) Long Paper: Papers are limited to 14 pages excluding references and will be included in the official ECCV proceedings, Please use the ECCV template ; (2) Short Papers: Papers are limited to 4 pages including references and will NOT be included in the official ECCV proceedings (does not count as double submission for most vision conferences). Please use the CVPR template for the short papers.

    Submissions Website:

    https://cmt3.research.microsoft.com/AROW2022/Submission/Index

    Important dates:

  • Submission deadline: August 1, 2022
  • Notification to authors: August 12, 2022
  • Camera-ready: August 15, 2022
  • Workshop: October 2022

  • Speakers

    Hima Lakkaraju
    Harvard University
    Olga Russakovsky
    Princeton University
    Cihang Xie
    UCSC
    Alan Yuille
    Johns Hopkins University

    Organizing Committee


    Sponsor


    Related Workshops

    Uncertainty & Robustness in Deep Learning (Workshop at ICML 2021)

    Security and Safety in Machine Learning Systems (Workshop at ICLR 2021)

    Generalization beyond the Training Distribution in Brains and Machines (Workshop at ICLR 2021)

    1st International Workshop on Adversarial Learning for Multimedia (Workshop at ACM Multimedia 2021)

    Workshop on Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges (Workshop at CVPR 2021)


    Please contact Angtian Wang or Yutong Bai if you have questions. The webpage template is by the courtesy of ECCV 2020 Workshop on Adversarial Robustness in the Real World.