From: AAAI.org/2022/Workshop/AdvML/Program_Chairs
title: Paper Decision
decision: Accept (Oral)
comment: Both reviewers give high scores on this paper. Thus it is accepted as an oral presentation.

From: AAAI.org/2022/Workshop/AdvML/Paper32/Reviewer_LPPi
title: A novel method of empirical attacks and defenses using semantic transformations
review: This paper proposed novel defense and attack methods using image transformations. The random transformation defense is designed by averaging the scores of results under random transformations. This paper proposed to use Bayesian optimization to select the most informative transformation parameters. The authors also re-implemented and evaluate BaRT attacks and improve their performance.

The experimental results and solid and significant so I recommend a clear acceptance.


rating: 8: Top 50% of accepted papers, clear accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct

From: AAAI.org/2022/Workshop/AdvML/Paper32/Reviewer_vUox
title: A more reliable evaluation of robustness for random transformation defenses
review: The authors show that backward-pass differentiable approximation struggles to approximate some transformations accurately, and non-differentiable transformation can’t guarantee robustness. Furthermore, the authors propose a novel attack method to evaluate random transformation defenses. This work provides a more reliable evaluation of robustness for random transformation defenses.
rating: 7: Good paper, accept
confidence: 5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature