Extremely large image generators offer significant transformative potential
across diverse sectors. It allows users to design specific prompts to generate
realistic images through some black-box APIs. However, some studies reveal that
image generators are notably susceptible to attacks and generate Not Suitable
For Work (NSFW) contents by manually designed toxin texts, especially
imperceptible to human observers. We urgently need a multitude of universal and
transferable prompts to improve the safety of image generators, especially
black-box-released APIs. Nevertheless, they are constrained by labor-intensive
design processes and heavily reliant on the quality of the given instructions.
To achieve this, we introduce a black-box stealthy prompt attack (BSPA) that
adopts a retriever to simulate attacks from API users. It can effectively
harness filter scores to tune the retrieval space of sensitive words for
matching the input prompts, thereby crafting stealthy prompts tailored for
image generators. Significantly, this approach is model-agnostic and requires
no internal access to the model's features, ensuring its applicability to a
wide range of image generators. Building on BSPA, we have constructed an
automated prompt tool and a comprehensive prompt attack dataset (NSFWeval).
Extensive experiments demonstrate that BSPA effectively explores the security
vulnerabilities in a variety of state-of-the-art available black-box models,
including Stable Diffusion XL, Midjourney, and DALL-E 2/3. Furthermore, we
develop a resilient text filter and offer targeted recommendations to ensure
the security of image generators against prompt attacks in the future.

通过黑盒 API，利用万能和可迁移的提示来提高图像生成器的安全性，并建立了一个自动提示工具和一个全面的提示攻击数据集，以探究各种黑盒模型的安全漏洞。

BSPA：探索图像生成器的黑盒隐秘提示攻击

BSPA: Exploring Black-box Stealthy Prompt Attacks against Image  Generators

Data-free meta-learning (DFML) aims to enable efficient learning of new tasks
by meta-learning from a collection of pre-trained models without access to the
training data. Existing DFML work can only meta-learn from (i) white-box and
(ii) small-scale pre-trained models (iii) with the same architecture,
neglecting the more practical setting where the users only have inference
access to the APIs with arbitrary model architectures and model scale inside.
To solve this issue, we propose a Bi-level Data-free Meta Knowledge
Distillation (BiDf-MKD) framework to transfer more general meta knowledge from
a collection of black-box APIs to one single meta model. Specifically, by just
querying APIs, we inverse each API to recover its training data via a
zero-order gradient estimator and then perform meta-learning via a novel
bi-level meta knowledge distillation structure, in which we design a boundary
query set recovery technique to recover a more informative query set near the
decision boundary. In addition, to encourage better generalization within the
setting of limited API budgets, we propose task memory replay to diversify the
underlying task distribution by covering more interpolated tasks. Extensive
experiments in various real-world scenarios show the superior performance of
our BiDf-MKD framework.

提出一种 Bi-level Data-free Meta Knowledge Distillation (BiDf-MKD) 框架，通过 black-box APIs 进行元学习，通过边缘查询集回归技术恢复更详细的查询集，并设计任务记忆重放技术以在有限的 API 预算内促进更好的泛化。