We welcome submission from different aspects of adversarial ML, including but not limited to

• Adversarial / poisoned attacks against ML models
• Adversarial defenses to improve decision robustness
• Methods of detecting / rejecting adversarial examples
• Model verification and certified training / inference
• Benchmarks to reliably evaluate previous defenses
• Theoretical understanding of adversarial ML
• Empirical studies that help to construct practically robust systems
• Adversarial ML in the real world
• Robust model architectures, data augmentations, and dataset biases
• Positive applications of the techniques in adversarial ML (e.g., privacy protection, generalization improvement, interpretable ML, transfer learning, reinforcement learning, traditional CV and NLP tasks)

We only consider submissions that haven’t been published in any peer-reviewed venue, including ICML 2021 conference. We welcome submissions that are currently under review in some conferences (e.g., NeurIPS 2021) The workshop is non-archival and will not have any official proceedings. Based on the PC’s recommendation, the accepted papers will be allocated either a contributed talk or a poster presentation.

Submission format:   Submissions should be anonymized and follow the template. Submissions should be up to 4 pages, plus unlimited space for references and appendices.

The list of accepted papers is available at https://openreview.net/group?id=ICML.cc/2021/Workshop/AML.

Best Paper Award: Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them by Florian Tramer

Adversarial for Good Award: Defending against Model Stealing via Verifying Embedded External Features by Linghui Zhu, Yiming Li, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, Xiaochun Cao

Silver Best Paper Awards (Runner-up):

• Adversarial Robustness of Streaming Algorithms through Importance Sampling by Vladimir Braverman, Avinatan Hassidim, Yossi Matias, Mariano Schain, Sandeep Silwal, Samson Zhou
• Demystifying Adversarial Training via A Unified Probabilistic Framework by Yifei Wang, Yisen Wang, Jiansheng Yang, Zhouchen Lin
• Certified robustness against adversarial patch attacks via randomized cropping by Wan-Yi Lin, Fatemeh Sheikholeslami, jinghao shi, Leslie Rice, J Zico Kolter
• Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off by Rahul Rade, Seyed-Mohsen Moosavi-Dezfooli

(Those awards are kindly sponsored by RealAI)