The high level of photorealism in state-of-the-art diffusion models like
Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained
humans to distinguish between real photographs and AI-generated images. To
address this problem, we designed a guide to help readers develop a more
critical eye toward identifying artifacts, inconsistencies, and
implausibilities that often appear in AI-generated images. The guide is
organized into five categories of artifacts and implausibilities: anatomical,
stylistic, functional, violations of physics, and sociocultural. For this
guide, we generated 138 images with diffusion models, curated 9 images from
social media, and curated 42 real photographs. These images showcase the kinds
of cues that prompt suspicion towards the possibility an image is AI-generated
and why it is often difficult to draw conclusions about an image's provenance
without any context beyond the pixels in an image. Human-perceptible artifacts
are not always present in AI-generated images, but this guide reveals artifacts
and implausibilities that often emerge. By drawing attention to these kinds of
artifacts and implausibilities, we aim to better equip people to distinguish
AI-generated images from real photographs in the future.

这篇研究论文介绍了一种指南，帮助读者更好地识别 AI 生成图像中的伪造和不合理之处，以区分真实照片和 AI 生成图像。

如何区分人工智能生成的图像与真实照片

How to Distinguish AI-Generated Images from Authentic Photographs

Anomaly detection (such as telecom fraud detection and medical image
detection) has attracted the increasing attention of people. The complex
interaction between multiple entities widely exists in the network, which can
reflect specific human behavior patterns. Such patterns can be modeled by
higher-order network structures, thus benefiting anomaly detection on
attributed networks. However, due to the lack of an effective mechanism in most
existing graph learning methods, these complex interaction patterns fail to be
applied in detecting anomalies, hindering the progress of anomaly detection to
some extent. In order to address the aforementioned issue, we present a
higher-order structure based anomaly detection (GUIDE) method. We exploit
attribute autoencoder and structure autoencoder to reconstruct node attributes
and higher-order structures, respectively. Moreover, we design a graph
attention layer to evaluate the significance of neighbors to nodes through
their higher-order structure differences. Finally, we leverage node attribute
and higher-order structure reconstruction errors to find anomalies. Extensive
experiments on five real-world datasets (i.e., ACM, Citation, Cora, DBLP, and
Pubmed) are implemented to verify the effectiveness of GUIDE. Experimental
results in terms of ROC-AUC, PR-AUC, and Recall@K show that GUIDE significantly
outperforms the state-of-art methods.

我们提出了一种基于高阶结构的异常检测方法（GUIDE），利用属性自编码器和结构自编码器对节点属性和高阶结构进行重建，并通过图注意力层评估邻居节点对节点的重要性，最终利用节点属性和高阶结构重建误差来寻找异常。在五个真实数据集上进行了广泛的实验，实验结果表明，GUIDE 在 ROC-AUC、PR-AUC 和 Recall@K 等指标上明显优于现有方法。

基于高阶结构的属性网络异常检测

Higher-order Structure Based Anomaly Detection on Attributed Networks

As a way to implement the "right to be forgotten" in machine learning,
\textit{machine unlearning} aims to completely remove the contributions and
information of the samples to be deleted from a trained model without affecting
the contributions of other samples. Recently, many frameworks for machine
unlearning have been proposed, and most of them focus on image and text data.
To extend machine unlearning to graph data, \textit{GraphEraser} has been
proposed. However, a critical issue is that \textit{GraphEraser} is
specifically designed for the transductive graph setting, where the graph is
static and attributes and edges of test nodes are visible during training. It
is unsuitable for the inductive setting, where the graph could be dynamic and
the test graph information is invisible in advance. Such inductive capability
is essential for production machine learning systems with evolving graphs like
social media and transaction networks. To fill this gap, we propose the
\underline{{\bf G}}\underline{{\bf U}}ided \underline{{\bf I}}n\underline{{\bf
D}}uctiv\underline{{\bf E}} Graph Unlearning framework (GUIDE). GUIDE consists
of three components: guided graph partitioning with fairness and balance,
efficient subgraph repair, and similarity-based aggregation. Empirically, we
evaluate our method on several inductive benchmarks and evolving transaction
graphs. Generally speaking, GUIDE can be efficiently implemented on the
inductive graph learning tasks for its low graph partition cost, no matter on
computation or structure information. The code will be available here:
this https URL

该研究提出了一种在归纳图学习任务中高效实现的、适用于动态图的图表征删除框架 GUIDE，它由三个组件组成：具有公平性和平衡性的引导图分区，高效的子图修复以及基于相似性的聚合。