Towards Transferable Unrestricted Adversarial Examples with Minimum Changes

TL;DR

This paper introduces a geometry-aware framework for generating transferable unrestricted adversarial examples with minimal perceptible changes, balancing transferability and imperceptibility effectively.

Contribution

It proposes a novel validation-based method to select optimal perturbation budgets for each image, improving transferability of adversarial examples under both $ ext{l}_ ext{infty}$ and unrestricted threat models.

Findings

Achieved state-of-the-art transfer success rates on ImageNet.

Ranked 1st in CVPR'21 Security AI Challenger with significant improvements.

Demonstrated effective balancing of imperceptibility and transferability.

Abstract

Transfer-based adversarial example is one of the most important classes of black-box attacks. However, there is a trade-off between transferability and imperceptibility of the adversarial perturbation. Prior work in this direction often requires a fixed but large $\ell_p$-norm perturbation budget to reach a good transfer success rate, leading to perceptible adversarial perturbations. On the other hand, most of the current unrestricted adversarial attacks that aim to generate semantic-preserving perturbations suffer from weaker transferability to the target model. In this work, we propose a geometry-aware framework to generate transferable adversarial examples with minimum changes. Analogous to model selection in statistical machine learning, we leverage a validation model to select the best perturbation budget for each image under both the $\ell_{\infty}$-norm and unrestricted threat models. We propose a principled method for the partition of training and validation models by encouraging intra-group diversity while penalizing extra-group similarity. Extensive experiments verify the effectiveness of our framework on balancing imperceptibility and transferability of the crafted adversarial examples. The methodology is the foundation of our entry to the CVPR'21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet, in which we ranked 1st place out of 1,559 teams and surpassed the runner-up submissions by 4.59% and 23.91% in terms of final score and average image quality level, respectively. Code is available at https://github.com/Equationliu/GA-Attack.