Graph Neural Networks (GNNs) have shown remarkable performance in various
tasks. However, recent works reveal that GNNs are vulnerable to backdoor
attacks. Generally, backdoor attack poisons the graph by attaching backdoor
triggers and the target class label to a set of nodes in the training graph. A
GNN trained on the poisoned graph will then be misled to predict test nodes
attached with trigger to the target class. Despite their effectiveness, our
empirical analysis shows that triggers generated by existing methods tend to be
out-of-distribution (OOD), which significantly differ from the clean data.
Hence, these injected triggers can be easily detected and pruned with widely
used outlier detection methods in real-world applications. Therefore, in this
paper, we study a novel problem of unnoticeable graph backdoor attacks with
in-distribution (ID) triggers. To generate ID triggers, we introduce an OOD
detector in conjunction with an adversarial learning strategy to generate the
attributes of the triggers within distribution. To ensure a high attack success
rate with ID triggers, we introduce novel modules designed to enhance trigger
memorization by the victim model trained on poisoned graph. Extensive
experiments on real-world datasets demonstrate the effectiveness of the
proposed method in generating in distribution triggers that can by-pass various
defense strategies while maintaining a high attack success rate.

本文研究了一种新颖的问题，即使用分布内的触发器进行不可察觉的图形后门攻击。为了生成分布内的触发器，我们引入了一个离群点检测器和对抗性学习策略，以在分布内生成触发器的属性。在真实世界的数据集上进行的大量实验证明了所提方法在生成可以绕过各种防御策略的分布内触发器的同时，能够保持高攻击成功率。

重思图形后门攻击：维持分布的视角

Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective

In Generalized Category Discovery (GCD), we cluster unlabeled samples of
known and novel classes, leveraging a training dataset of known classes. A
salient challenge arises due to domain shifts between these datasets. To
address this, we present a novel setting: Across Domain Generalized Category
Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer Across Domains)
as a remedy. CDAD-NET is architected to synchronize potential known class
samples across both the labeled (source) and unlabeled (target) datasets, while
emphasizing the distinct categorization of the target data. To facilitate this,
we propose an entropy-driven adversarial learning strategy that accounts for
the distance distributions of target samples relative to source-domain class
prototypes. Parallelly, the discriminative nature of the shared space is upheld
through a fusion of three metric learning objectives. In the source domain, our
focus is on refining the proximity between samples and their affiliated class
prototypes, while in the target domain, we integrate a neighborhood-centric
contrastive learning mechanism, enriched with an adept neighborsmining
approach. To further accentuate the nuanced feature interrelation among
semantically aligned images, we champion the concept of conditional image
inpainting, underscoring the premise that semantically analogous images prove
more efficacious to the task than their disjointed counterparts.
Experimentally, CDAD-NET eclipses existing literature with a performance
increment of 8-15% on three AD-GCD benchmarks we present.

在跨领域广义类别发现中，CDAD-NET 通过对源域和目标域之间的样本进行同步，强调目标数据的独特分类，并且通过三个度量学习目标的融合来保持共享空间的区分性。在实验中，CDAD-NET 在三个 AD-GCD 基准测试中表现出 8-15％的性能提升。

CDAD-Net: 广义类别发现中的领域差距连接

CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery

We present PointAugment, a new auto-augmentation framework that automatically
optimizes and augments point cloud samples to enrich the data diversity when we
train a classification network. Different from existing auto-augmentation
methods for 2D images, PointAugment is sample-aware and takes an adversarial
learning strategy to jointly optimize an augmentor network and a classifier
network, such that the augmentor can learn to produce augmented samples that
best fit the classifier. Moreover, we formulate a learnable point augmentation
function with a shape-wise transformation and a point-wise displacement, and
carefully design loss functions to adopt the augmented samples based on the
learning progress of the classifier. Extensive experiments also confirm
PointAugment's effectiveness and robustness to improve the performance of
various networks on shape classification and retrieval.

这篇论文提出了 PointAugment 自动增强框架，使用对抗性学习策略自动优化和增强点云样本以增加数据多样性，在形状分类方面取得良好表现。