Graph Neural Networks(GNNs) are vulnerable to adversarial attack that cause
performance degradation by adding small perturbations to the graph.
Gradient-based attacks are one of the most commonly used methods and have
achieved good performance in many attack scenarios. However, current gradient
attacks face the problems of easy to fall into local optima and poor attack
invisibility. Specifically, most gradient attacks use greedy strategies to
generate perturbations, which tend to fall into local optima leading to
underperformance of the attack. In addition, many attacks only consider the
effectiveness of the attack and ignore the invisibility of the attack, making
the attacks easily exposed leading to failure. To address the above problems,
this paper proposes an attack on GNNs, called AGSOA, which consists of an
average gradient calculation and a structre optimization module. In the average
gradient calculation module, we compute the average of the gradient information
over all moments to guide the attack to generate perturbed edges, which
stabilizes the direction of the attack update and gets rid of undesirable local
maxima. In the structure optimization module, we calculate the similarity and
homogeneity of the target node's with other nodes to adjust the graph structure
so as to improve the invisibility and transferability of the attack. Extensive
experiments on three commonly used datasets show that AGSOA improves the
misclassification rate by 2$\%$-8$\%$ compared to other state-of-the-art
models.

该研究提出了一种对图神经网络 (GNNs) 进行攻击的方法，称为 AGSOA，该方法通过平均梯度计算和结构优化模块来提高攻击的性能。通过在所有时刻计算梯度信息的平均值来引导攻击生成扰动边，从而稳定攻击更新的方向并避免不良局部最大值；通过计算目标节点与其他节点的相似性和均匀性来调整图结构，从而提高攻击的隐蔽性和可转移性。在三个常用数据集上的实验证明，相比其他先进模型，AGSOA 提高了误分类率 2% 至 8%。

AGSOA：基于平均梯度和结构优化的图神经网络目标攻击

AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and  Structure Optimization

Generative image modeling enables a wide range of applications but raises
ethical concerns about responsible deployment. This paper introduces an active
strategy combining image watermarking and Latent Diffusion Models. The goal is
for all generated images to conceal an invisible watermark allowing for future
detection and/or identification. The method quickly fine-tunes the latent
decoder of the image generator, conditioned on a binary signature. A
pre-trained watermark extractor recovers the hidden signature from any
generated image and a statistical test then determines whether it comes from
the generative model. We evaluate the invisibility and robustness of the
watermarks on a variety of generation tasks, showing that Stable Signature
works even after the images are modified. For instance, it detects the origin
of an image generated from a text prompt, then cropped to keep $10\%$ of the
content, with $90$+$\%$ accuracy at a false positive rate below 10$^{-6}$.

本文提出了一种将图像水印和潜在扩散模型相结合的主动策略，以实现所有生成的图像具有隐匿的水印，从而便于将来的检测和 / 或识别，评估了水印在各种生成任务中的隐身性和鲁棒性，显示稳定签名即使在被修改之后仍可使用，能够在误识率低于 10 的负 6 次方以下时，准确率达到 90% 以上，能够检测文本提示生成的图像来源并对图像进行裁剪处理的情况。

稳定签名：将水印扎根于潜在扩散模型中

The Stable Signature: Rooting Watermarks in Latent Diffusion Models

Backdoor attacks are a kind of insidious security threat against machine
learning models. After being injected with a backdoor in training, the victim
model will produce adversary-specified outputs on the inputs embedded with
predesigned triggers but behave properly on normal inputs during inference. As
a sort of emergent attack, backdoor attacks in natural language processing
(NLP) are investigated insufficiently. As far as we know, almost all existing
textual backdoor attack methods insert additional contents into normal samples
as triggers, which causes the trigger-embedded samples to be detected and the
backdoor attacks to be blocked without much effort. In this paper, we propose
to use the syntactic structure as the trigger in textual backdoor attacks. We
conduct extensive experiments to demonstrate that the syntactic trigger-based
attack method can achieve comparable attack performance (almost 100% success
rate) to the insertion-based methods but possesses much higher invisibility and
stronger resistance to defenses. These results also reveal the significant
insidiousness and harmfulness of textual backdoor attacks. All the code and
data of this paper can be obtained at this https URL

本文研究基于句法结构的文字后门攻击方法，实验表明该方法具有相当的攻击性能，同时具有更高的隐蔽性和抵御防御的能力。