In the scenario of black-box adversarial attack, the target model's
parameters are unknown, and the attacker aims to find a successful adversarial
perturbation based on query feedback under a query budget. Due to the limited
feedback information, existing query-based black-box attack methods often
require many queries for attacking each benign example. To reduce query cost,
we propose to utilize the feedback information across historical attacks,
dubbed example-level adversarial transferability. Specifically, by treating the
attack on each benign example as one task, we develop a meta-learning framework
by training a meta-generator to produce perturbations conditioned on benign
examples. When attacking a new benign example, the meta generator can be
quickly fine-tuned based on the feedback information of the new task as well as
a few historical attacks to produce effective perturbations. Moreover, since
the meta-train procedure consumes many queries to learn a generalizable
generator, we utilize model-level adversarial transferability to train the
meta-generator on a white-box surrogate model, then transfer it to help the
attack against the target model. The proposed framework with the two types of
adversarial transferability can be naturally combined with any off-the-shelf
query-based attack methods to boost their performance, which is verified by
extensive experiments.

文章提出了利用历史攻击反馈信息来降低黑盒对抗攻击的查询成本，通过开发一个元学习框架来训练元 Perturbation 生成器，以产生有效的干扰，同时利用模型级对抗可迁移性来训练元生成器，以帮助攻击目标模型。这个框架可以与任何现成的基于查询攻击方法结合使用，以提高攻击性能。

元学习通用黑盒对抗攻击

Generalizable Black-Box Adversarial Attack with Meta Learning

Highly skewed long-tail item distribution is very common in recommendation
systems. It significantly hurts model performance on tail items. To improve
tail-item recommendation, we conduct research to transfer knowledge from head
items to tail items, leveraging the rich user feedback in head items and the
semantic connections between head and tail items. Specifically, we propose a
novel dual transfer learning framework that jointly learns the knowledge
transfer from both model-level and item-level: 1. The model-level knowledge
transfer builds a generic meta-mapping of model parameters from few-shot to
many-shot model. It captures the implicit data augmentation on the model-level
to improve the representation learning of tail items. 2. The item-level
transfer connects head and tail items through item-level features, to ensure a
smooth transfer of meta-mapping from head items to tail items. The two types of
transfers are incorporated to ensure the learned knowledge from head items can
be well applied for tail item representation learning in the long-tail
distribution settings. Through extensive experiments on two benchmark datasets,
results show that our proposed dual transfer learning framework significantly
outperforms other state-of-the-art methods for tail item recommendation in hit
ratio and NDCG. It is also very encouraging that our framework further improves
head items and overall performance on top of the gains on tail items.

提出了一种基于双重转移学习框架的推荐系统，其中模型参数和项目特征从头部项目中转移，以改善在长尾分布情况下的尾部项目推荐性能。