Adversarial attacks provide a good way to study the robustness of deep
learning models. One category of methods in transfer-based black-box attack
utilizes several image transformation operations to improve the transferability
of adversarial examples, which is effective, but fails to take the specific
characteristic of the input image into consideration. In this work, we propose
a novel architecture, called Adaptive Image Transformation Learner (AITL),
which incorporates different image transformation operations into a unified
framework to further improve the transferability of adversarial examples.
Unlike the fixed combinational transformations used in existing works, our
elaborately designed transformation learner adaptively selects the most
effective combination of image transformations specific to the input image.
Extensive experiments on ImageNet demonstrate that our method significantly
improves the attack success rates on both normally trained models and defense
models under various settings.

本文提出了一种名为自适应图片转换学习器（AITL）的新型结构，该结构将不同的图片转换操作集成到统一的框架中，进一步提高对抗样本的可转移性，实验表明该方法在 ImageNet 上的攻击成功率显著提高。