Results 281 to 290 of about 1,209,773 (317)

Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey

IEEE Communications Surveys and Tutorials, 2023
Adversarial attacks and defenses in machine learning and deep neural network (DNN) have been gaining significant attention due to the rapidly growing applications of deep learning in communication networks.
Yulong Wang, Tong Sun, Shenghong Li, Xinnan Yuan, W. Ni, Ekram Hossain, H. Vincent Poor   +6 more
semanticscholar   +1 more source

OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

IEEE transactions on intelligent transportation systems (Print), 2023
Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems
Waleed Yamany, Nour Moustafa, B. Turnbull   +2 more
semanticscholar   +1 more source

Robust Load Forecasting Towards Adversarial Attacks via Bayesian Learning

IEEE Transactions on Power Systems, 2023
Electric load forecasting is an essential problem for the power industry, which has a significant impact on power system operation. Currently, deep learning is proved to be an effective tool for load forecasting.
Yihong Zhou, Zhaohao Ding, Qingsong Wen, Yi Wang   +3 more
semanticscholar   +1 more source

ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale

The Automatic Speaker Verification Spoofing Countermeasures Workshop (ASVspoof 2024)
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions.
Xin Wang, Héctor Delgado, Hemlata Tak, Jee-weon Jung, Hye-jin Shim, M. Todisco, Ivan Kukanov, Xuechen Liu, Md. Sahidullah, Tomi Kinnunen, Nicholas W. D. Evans, K. Lee, Junichi Yamagishi   +12 more
semanticscholar   +1 more source

Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment

Conference on Empirical Methods in Natural Language Processing
Large Language Models (LLMs) are powerful zero-shot assessors used in real-world situations such as assessing written exams and benchmarking systems. Despite these critical applications, no existing work has analyzed the vulnerability of judge-LLMs to ...
Vyas Raina, Adian Liusie, Mark J. F. Gales   +2 more
semanticscholar   +1 more source

Sinkhorn Adversarial Attack and Defense

IEEE Transactions on Image Processing, 2022
Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance.
openaire   +2 more sources

Efficient Adversarial Training in LLMs with Continuous Attacks

Neural Information Processing Systems
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet,
Sophie Xhonneux, Alessandro Sordoni, Stephan Günnemann, G. Gidel, Leo Schwinn   +4 more
semanticscholar   +1 more source

Robust Graph Convolutional Networks Against Adversarial Attacks

Knowledge Discovery and Data Mining, 2019
Graph Convolutional Networks (GCNs) are an emerging type of neural network model on graphs which have achieved state-of-the-art performance in the task of node classification.
Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu   +3 more
semanticscholar   +1 more source

Audio adversarial attack: HIS attack

International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022
Jian Ma, Da Luo
openaire   +1 more source

AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models

arXiv.org, 2023
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, A. Nenkova, Tong Sun   +8 more
semanticscholar   +1 more source