Text-attributed graph fraud detection (TAGFD) plays a critical role in preventing fraudulent activities on online social and e-commerce platforms. However, to evade detection, fraudsters continuously evolve their camouflaging strategies by deliberately mimicking textual responses of benign users, thereby concealing their malicious purposes. This phenomenon, referred to as semantic camouflage, fundamentally undermines commonly relied assumptions on how structural and attribute cues can be exploited to identify fraudsters, and makes it difficult to spot fraudsters with unsupervised TAGFD. To bridge the gaps, we propose a Case-Adaptive Multi-cue Expert fRAmework (CAMERA) for unsupervised TAGFD. CAMERA employs an ego-decoupled mixture-of-experts architecture, where each expert specializes in modeling a distinct type of fraud-indicative cue. A context-informed gating model is introduced to jointly consider the ego node representation and its local neighborhood context for adaptive integration of cues learned by different experts. Furthermore, CAMERA leverages the inherent rarity of fraudsters to support unsupervised one-class learning with expert-level objectives that encourage modeling dominant benign patterns, thereby enabling reliable unsupervised detection of camouflaged fraudsters. Experiments on 4 challenging datasets show that CAMERA consistently outperforms competitors, showing its effectiveness against semantically camouflaged fraudsters. Code available at https://github.com/CampanulaBells/CAMERA

翻译：摘要：文本属性图谱欺诈检测（TAGFD）在预防在线社交与电商平台欺诈活动中发挥关键作用。然而，为逃避检测，欺诈者通过刻意模仿良性用户的文本响应不断演化其伪装策略，从而隐藏恶意意图。这种被称为语义伪装的现象从根本上动摇了如何利用结构特征与属性线索识别欺诈者的常规假设，使得基于无监督TAGFD的欺诈识别变得困难。为弥补这一不足，我们提出一种针对无监督TAGFD的案例自适应多线索专家框架（CAMERA）。该框架采用自我解耦的混合专家架构，每位专家专门建模一类欺诈指示线索。通过引入上下文感知门控模型，该模型联合考虑自我节点表示及其局部邻域上下文，实现不同专家所学习线索的自适应整合。此外，CAMERA利用欺诈者的固有稀缺性支持基于专家级目标的无监督单类学习，激励模型学习主导性良性模式，从而实现对伪装欺诈者的可靠无监督检测。在四个具有挑战性的数据集上的实验表明，CAMERA始终优于现有方法，验证了其对抗语义伪装欺诈者的有效性。代码开源地址：https://github.com/CampanulaBells/CAMERA