Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL

• URL: http://arxiv.org/abs/2608.29054v1
• Date: Sat, 29 Aug 2026 05:08:45 GMT
• Title: Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL
• Abstract summary: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks.<n>We propose a transferable graph purification scheme, named ProGAP, to bridge adversarial defense knowledge via vulnerability-aware graph prompt learning.<n>ProGAP achieves 1%-9% improvement, and reduces the time consumption by up to 2.2x.
• Score: 20.70305524296098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone for representing complex relational dependencies in diverse multimedia tasks, particularly in cross-platform user interest modeling and cross-modal semantic alignment. In the real world, a practical defense against graph adversarial perturbations is needed. However, we observe that the prevailing adversarial purification methods are essentially domain-restricted defenses, which leads to the following shortcomings: (1) single-domain data provides insufficient structural and semantic diversity for learning robust purification criteria; (2) training of domain-specific defense strategies from scratch consumes substantial computational cost. To address the above limitations, we propose a transferable graph purification scheme, named ProGAP, to bridge adversarial defense knowledge via vulnerability-aware graph prompt learning. Firstly, to capture universal adversarial patterns, a perturbation-capture edge detector is pretrained on data-rich graphs by jointly modeling topological and semantic information. Subsequently, to achieve more knowledge transfer w.r.t. robustness, vulnerability-aware prompts are designed that inject targeted purification guidance into biased nodes, during which the pretrained detector adapts to distribution shifts in downstream graphs without parameter-laborious updates. Experimental results demonstrate that compared with state-of-the-art baselines, our ProGAP achieves 1%-9% improvement, and reduces the time consumption by up to 2.2x. The code for ProGAP is available at https://github.com/Lieyoufffff/ProGAP.

Related papers

• Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks [2.434167379974669]
  This paper proposes a Graph-based Generative Adrial Network (GraphGAN) that serves as the cognitive detection engine for this task.<n>GraphGAN captures the relational structure among traffic flows while addressing imbalance through adversarial generation of synthetic samples.<n>The generator learns the distribution of DDoS attacks to synthesize realistic minority samples, while a Graph Convolutional Network (GCN)-based discriminator distinguishes real from synthetic graph data.
  arXiv  Detail & Related papers  (2026-08-18T04:18:31Z)
• Defending against Model Extraction for GNNs with Model Reprogramming [57.82196268649236]
  Graph Neural Networks (GNNs) serve as the backbone for high-stakes applications in Machine-Learning-as-a-Service (ML)<n>Still, their black-box deployment exposes them to Model Extraction (ME) attacks.<n>Existing defenses suffer from a critical ''Euclidean bias''<n>We propose GraphRP, a proactive defense framework that repurposes Model Reprogramming for security.
  arXiv  Detail & Related papers  (2026-08-11T23:20:15Z)
• Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense [80.28330585803133]
  Graph neural networks (GNNs) are widely deployed on computation data, yet they can leak sensitive or proprietary information about the training graph adjacency.<n>This work studies graph reconstruction attacks (GRA), a form of model inversion that reconstructs the training adjacency from a trained GNN.<n>We propose MC-GRA (+), which reconstructs the adjacency by optimizing a surrogate adjacency whose GNN-induced representations align with those of the target model.<n>On the defense side, we propose MC-GPB (+), which suppresses adjacency-dependent information throughout the representation chain
  arXiv  Detail & Related papers  (2026-06-06T09:29:27Z)
• Self-supervised Adversarial Purification for Graph Neural Networks [8.721619913104899]
  Defending Graph Neural Networks (GNNs) against adversarial attacks requires balancing accuracy and robustness.<n>We propose a self-supervised adversarial purification framework to overcome this limitation.<n>In contrast to prior adversarial purification methods, we propose GPR-GAE as a specialized purifier trained with a self-supervised strategy.
  arXiv  Detail & Related papers  (2026-05-22T05:18:18Z)
• PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing [7.3809340470076465]
  We propose PEANUT, a gradient-free, restricted black-box attack that injects virtual nodes to capitalize on a vulnerability in Graph Neural Networks (GNNs)<n>PEANUT is a injection based attack, which is widely considered to be more practical and realistic scenario than graph modification attacks.<n>Experiments on real-world datasets across three graph tasks demonstrate the effectiveness of our attack despite its simplicity.
  arXiv  Detail & Related papers  (2026-03-27T07:40:10Z)
• Everything Perturbed All at Once: Enabling Differentiable Graph Attacks [61.61327182050706]
  Graph neural networks (GNNs) have been shown to be vulnerable to adversarial attacks. We propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint.
  arXiv  Detail & Related papers  (2023-08-29T20:14:42Z)
• EDoG: Adversarial Edge Detection For Graph Neural Networks [17.969573886307906]
  Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. Recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. We propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation.
  arXiv  Detail & Related papers  (2022-12-27T20:42:36Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Information Obfuscation of Graph Neural Networks [96.8421624921384]
  We study the problem of protecting sensitive attributes by information obfuscation when learning with graph structured data. We propose a framework to locally filter out pre-determined sensitive attributes via adversarial training with the total variation and the Wasserstein distance.
  arXiv  Detail & Related papers  (2020-09-28T17:55:04Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.