Explainability-based backdoor attacks
WebTo bridge this gap, we conduct an experimental investigation on the performance of backdoor attacks on GNNs. We apply two powerful GNN explainability approaches to … WebDec 30, 2024 · Deep neural networks have been shown to be vulnerable to backdoor attacks, which could be easily introduced to the training set prior to model training. Recent work has focused on investigating backdoor attacks on natural images or toy datasets. Consequently, the exact impact of backdoors is not yet fully understood in complex real …
Explainability-based backdoor attacks
Did you know?
WebApr 5, 2024 · Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance on the clean inputs. WebJun 13, 2024 · Explainability-based Backdoor Attacks Against Graph Neural Networks. Jing Xu, Minhui, Xue, and Stjepan Picek. arXiv, 2024. Point Cloud. A Backdoor Attack against 3D Point Cloud Classifiers. …
WebApr 8, 2024 · Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen … WebApr 8, 2024 · Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while performing normally on other benign inputs. There are already numerous works on backdoor attacks on neural networks, but only a few works consider graph …
WebCAM), a weakly-supervised explainability technique (Selvaraju et al. 2024). By showing how explainability can be used to identify the presence of a backdoor, we em-phasize the role of explainability in investigating model robustness. Related Work Earlier defense mechanisms against backdoor attacks often WebFeb 10, 2024 · Backdoor attack of graph neural networks based on subgraph trigger. In International Conference on Collaborative Computing: Networking, Applications and Worksharing. Springer, 276-296.
Webable to backdoor attacks. Zhang et al. proposed a subgraph-based backdoor attack to GNNs for graph classification task [18]. Xi et al. presented a subgraph-based backdoor …
WebApr 15, 2024 · This section discusses basic working principle of backdoor attacks and SOTA backdoor defenses such as NC [], STRIP [] and ABS [].2.1 Backdoor Attacks. … showcase productions amherstWebFeb 10, 2024 · Backdoor attack of graph neural networks based on subgraph trigger. In International Conference on Collaborative Computing: Networking, Applications and … showcase productions toursWebApr 8, 2024 · Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an attacker-chosen target label while performing normally on other benign inputs. There are already numerous works on backdoor attacks on neural networks, but only a few works consider graph … showcase productions bus toursWebSketchXAI: A First Look at Explainability for Human Sketches Zhiyu Qu · Yulia Gryaditskaya · Ke Li · Kaiyue Pang · Tao Xiang · Yi-Zhe Song Learning Geometry-aware … showcase products granite city ilWebExplainability-based Backdoor Attacks against Graph Neural Networks. Xu J; Xue M; Picek S; WiseML 2024 - Proceedings of the 3rd ACM Workshop on Wireless Security … showcase products llcWebApr 13, 2024 · Neural networks are vulnerable to various types of attacks, such as data poisoning, model stealing, adversarial examples, and backdoor insertion. These attacks can compromise the integrity ... showcase project meaningWebAbstract Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for ... showcase programs