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In this paper, a detection method is proposed for the adversarially-learned injection attacks via knowledge graphs.
Oct 18, 2024 · In this paper, a detection method is proposed for the adversarially-learned injection attacks via knowledge graphs. Firstly, with the advantages ...
Jun 15, 2024 · This paper studies the problem of injecting factual knowledge into large pre-trained language models. We train adapter modules on parts of the ...
Detecting the adversarially-learned injection attacks via knowledge graphs ... Revisiting Adversarially Learned Injection Attacks Against Recommender Systems.
Federated learning (FL) is vulnerable to backdoor attacks, which aim to cause the misclassification on samples with a specific backdoor.
Detecting the adversarially-learned injection attacks via knowledge graphs. 知識グラフを介した敵対的に学習されたインジェクション攻撃の検出【JST機械翻訳】.
DAAKG-Drop. Detecting the Adversarially-Learned Injection Attacks via Knowledge Graphs. Required packages. The code has been tested running under Python 3.8 ...
Detecting the adversarially-learned injection attacks via knowledge graphs · Yaojun Hao,; Haotian Wang,; Qingshan Zhao,; + 2. https://doi.org/10.1016/j.is ...
In the security arms race, a limited knowledge of the attack leads to a more dangerous state of existing systems. In this work, we aim to revisit this direction ...
Specifically, we introduce a hierarchical Q-learning network to manipulate the labels of the adversarial nodes and their links with other nodes in the graph, ...