Explainability-based backdoor attacks

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August undefined, 2024

WebView PDF. Download Free PDF. Download. Explainability Matters: Backdoor Attacks on Medical Imaging Munachiso Nwadike,*1 Takumi Miyawaki,*1 Esha Sarkar,2 Michail Maniatakos,1 Farah Shamout1 † 1 NYU Abu Dhabi, UAE 2 NYU Tandon School of Engineering, USA * Equal Contributions † [email protected] arXiv:2101.00008v1 [cs.CR] … WebExplainability-based Backdoor Attacks against Graph Neural Networks. Author. Xu, J. (TU Delft Cyber Security) Xue, Minhui (University of Adelaide) Picek, S. (TU Delft Cyber Security) Date. 2024. Abstract. Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger-embedded inputs into an ...

Explainability-based Backdoor Attacks Against Graph Neural Ne…

WebApr 5, 2024 · The results show that, generally, LIAS performs better, and the differences between the LIAS and MIAS performance can be significant, and these two strategies' similar (better) attack performance through explanation techniques, results in a further understanding of backdoor attacks in GNNs. Backdoor attacks have been … 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 is cali a state

Explainable artificial intelligence for cybersecurity: a literature ...

WebJan 1, 2024 · To address these problems, we study the GNN backdoor attack based on the subgraph trigger. We design the trigger based on the features of the sample data … WebJun 19, 2024 · Specifically, we propose a subgraph based backdoor attack to GNN based graph classification. In our backdoor attack, a GNN classifier predicts an attacker … WebApr 11, 2024 · Adversarial AI is not just traditional software development. There are marked differences between adversarial AI and traditional software development and cybersecurity frameworks. Often, vulnerabilities in ML models are connected back to data poisoning and other types of data-based attacks. Since these vulnerabilities are inherent in the model ... ruth becher bayreuth

Explainability Matters: Backdoor Attacks on Medical Imaging

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. … 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 … is caliban good or evilWebJun 28, 2024 · For example, in explainability-based backdoor attacks [95], GNNExplainer is employed [22] to identify the importance of nodes and guide the selection of the injected backdoor trigger position ... is caliber in inches

"WebOct 26, 2024 · Explainability (interpretability) can be defined as the ability to provide the meaning of the relationships a model’s inputs and its outcomes have, in a human-readable form [ 85 ]. In the XAI field, explainability (interpretability) is the degree to which the decision made by an AI model can be understood by humans. " - Explainability-based backdoor attacks

Explainability-based backdoor attacks

WebApr 9, 2024 · Examples of these attacks are clean-label poisoning and backdoor attacks. Backdoor attacks—the most interesting attacks—only misclassify inputs containing specific (explicit or even implicit) triggers. A trigger example for a DL model used for face-based authentication would be the presence of glasses with a certain shape. Web2 days ago · Backdoor attacks prey on the false sense of security that perimeter-based systems create and perpetuate. Edward Snowden’s book Permanent Record removed …

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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 …

WebJun 28, 2024 · To bridge this gap, we conduct an experimental investigation on the performance of backdoor attacks on GNNs. We apply two powerful GNN explainability … WebApr 10, 2024 · Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger. ... PCA-Based Knowledge Distillation Towards Lightweight and Content-Style Balanced Photorealistic Style Transfer Models. ... Explainability-Aided Image Classification and Generation.

WebApr 8, 2024 · Explainability-based Backdoor Attacks Against Graph Neural Networks. Backdoor attacks represent a serious threat to neural network models. A backdoored model will misclassify the trigger … WebSketchXAI: A First Look at Explainability for Human Sketches Zhiyu Qu · Yulia Gryaditskaya · Ke Li · Kaiyue Pang · Tao Xiang · Yi-Zhe Song Learning Geometry-aware Representations by Sketching ... Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning

WebExplainability-based Backdoor Attacks Against Graph Neural Networks. Backdoor attacks represent a serious threat to neural network models. A backdoored model will …

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. ruth bechtle pierce monroeville paWebSketchXAI: A First Look at Explainability for Human Sketches Zhiyu Qu · Yulia Gryaditskaya · Ke Li · Kaiyue Pang · Tao Xiang · Yi-Zhe Song Learning Geometry-aware … is calibri an accessible fontWebApr 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 … ruth beck artWebJun 28, 2024 · For example, in explainability-based backdoor attacks [95], GNNExplainer is employed [22] to identify the importance of nodes and guide the selection of the … ruth beatriceWebon the explainability of triggers for backdoor attacks on GNNs. Our contributions can be summarized as follows: •We utilize GNNExplainer, an approach for explaining pre … ruth became an ancestor of jesus. true falseWebDec 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. … ruth became a in the fields of boazWebApr 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. is califlronia and tijuana on same time zone