Graph Neural Networks (GNNs) have achieved remarkable success in various applications, while still exhibiting high vulnerability to backdoor attacks when applied to node classification. Existing single-category attack methods typically rely on adaptive triggers that force victim nodes to be misclassified into a fixed target label, but they often neglect the inherent structural and feature priors associated with the target category. In this work, we propose a novel and effective backdoor attack framework Category-Specific Trigger Backdoor Attacks (CSTBA), employing category-specific information to generate more natural and unnoticeable triggers. Specifically, we introduce a Category-Specific Subgraph Triggers Pool (CS-STP) to capture representative patterns of the target category, along with a Match-and-Attach Strategy (MAS) to unnoticeably attach triggers to victim nodes, thereby ensuring that the modifications remain effective and unnoticeable within the graph. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly enhances both the attack success rate (ASRs) and the unnoticeability of the attack compared with existing single-category approaches, highlighting the critical importance of category-aware trigger design in GNN backdoor attacks.

Semantic segmentation is one of the tasks concerned in the field of computer vision. However, the cost of capturing large numbers of pixel-level annotations is expensive. Semi-supervised learning can utilize labeled and unlabeled data, providing new ideas for solving the problem of insufficient labeled data. In this work, we propose a data-reliability weighted multi-phase learning method for semi-supervised segmentation (RWMS). Under the framework of self-training, we train two different teacher models to evaluate the reliability of pseudo labels. By selecting reliable data at the image level and reweighting pseudo labels at the pixel level, multi-phase training is guided to focus on more reliable knowledge. Besides, we also inject strong data augmentations on unlabeled images while training. Through extensive experiments, we demonstrate that our method performs remarkably well compared to baseline methods and substantially outperforms them, more than 3% on VOC and Cityscapes.