The fast advance of the image generation community has attracted attention
worldwide. The safety issue needs to be further scrutinized and studied. There
have been a few works around this area mostly achieving a post-processing
design, model-specific, or yielding suboptimal image quality generation.
Despite that, in this article, we discover a black-box attack method that
enjoys three merits. It enables (i)-attacks both directed and semantic-driven
that theoretically and practically pose a hazard to this vast user community,
(ii)-surprisingly surpasses the white-box attack in a black-box manner and
(iii)-without requiring any post-processing effort. Core to our approach is
inspired by the concept guidance intriguing property of Classifier-Free
guidance (CFG) in T2I models, and we discover that conducting frustratingly
simple guidance in the CLIP embedding space, coupled with the semantic loss and
an additionally sensitive word list works very well. Moreover, our results
expose and highlight the vulnerabilities in existing defense mechanisms.

通过黑盒攻击方法和分类器自由引导的概念，本研究暴露和强调了现有防御机制的脆弱性，这给图像生成社区带来了安全问题。

越狱快速攻击：一种针对扩散模型的可控对抗攻击

Jailbreaking Prompt Attack: A Controllable Adversarial Attack against  Diffusion Models

Recently, recommender systems that aim to suggest personalized lists of items
for users to interact with online have drawn a lot of attention. In fact, many
of these state-of-the-art techniques have been deep learning based. Recent
studies have shown that these deep learning models (in particular for
recommendation systems) are vulnerable to attacks, such as data poisoning,
which generates users to promote a selected set of items. However, more
recently, defense strategies have been developed to detect these generated
users with fake profiles. Thus, advanced injection attacks of creating more
`realistic' user profiles to promote a set of items is still a key challenge in
the domain of deep learning based recommender systems. In this work, we present
our framework CopyAttack, which is a reinforcement learning based black-box
attack method that harnesses real users from a source domain by copying their
profiles into the target domain with the goal of promoting a subset of items.
CopyAttack is constructed to both efficiently and effectively learn policy
gradient networks that first select, and then further refine/craft, user
profiles from the source domain to ultimately copy into the target domain.
CopyAttack's goal is to maximize the hit ratio of the targeted items in the
Top-$k$ recommendation list of the users in the target domain. We have
conducted experiments on two real-world datasets and have empirically verified
the effectiveness of our proposed framework and furthermore performed a
thorough model analysis.

本研究提出了一种名为 CopyAttack 的强化学习黑盒攻击方法，可以利用源域中的真实用户信息，在目标域中推广一组项目，从而最大化目标域中用户 Top-k 推荐列表中目标项目的点击率，并在实验中验证了该方法的有效性，并进行了深入的模型分析。