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:
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 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:
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)
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.