WebGraph Backdoor. Zhaohan Xi, Ren Pang, Shouling Ji, Ting Wang. arxiv 2024. Attacking Black-box Recommendations via Copying Cross-domain User Profiles. Wenqi Fan, Tyler … WebGraph Neural Networks (GNNs) have demonstrated their powerful capability in learning representations for graph-structured data. Consequently, they have enhanced the performance of many graph-related tasks such as node classification and graph classification. However, it is evident from recent studies that GNNs are vulnerable to …

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WebFeb 21, 2024 · This work proposes a novel graph backdoor attack that uses node features as triggers and does not need knowledge of the GNNs parameters, and finds that feature … Webgraphs, backdoor attacks inject triggers in the form of sub-graphs [18]. An adversary can launch backdoor attacks by manipulating the training data and corresponding labels. Fig. 2 illustrates the flow of a subgraph-based backdoor attack against GNNs. In this attack, a backdoor trigger and a target label y t are determined. foreboding in the bible

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WebInstance Relation Graph Guided Source-Free Domain Adaptive Object Detection Vibashan Vishnukumar Sharmini · Poojan Oza · Vishal Patel Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations ... Backdoor Defense via Deconfounded Representation Learning Zaixi Zhang · Qi Liu · Zhicai Wang · Zepu Lu · … WebOur empirical results on three real-world graph datasets show that our backdoor attacks are effective with a small impact on a GNN's prediction accuracy for clean testing … WebClause (iii) say that Xsatis es the back-door criterion for estimating the e ect of Son Y, and the inner sum in Eq. 2 is just the back-door estimate (Eq. 1) of Pr(Yjdo(S= s)). So really we are using the back door criterion. (See Figure 2.) Both the back-door and front-door criteria are su cient for estimating causal forebodings crossword clue