Results 51 to 60 of about 3,499,798 (281)
Name Entity Recognition based on Local Adversarial Training
四川大学学报. 自然科学版, 2021 Boundary samples of different categories staggered on the boundary in the datasets of named entity recognition research, which affects the performance of named entity recognition model. A method based on local adversarial training and BiLSTMCRF model is
LI Jing +3 more
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EIFDAA: Evaluation of an IDS with function-discarding adversarial attacks in the IIoT
Heliyon, 2023 The complexity of the Industrial Internet of Things (IIoT) presents higher requirements for intrusion detection systems (IDSs). An adversarial attack is a threat to the security of machine learning-based IDSs.
Shiming Li +4 more
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Connecting Certified and Adversarial Training
Advances in Neural Information Processing Systems 36, 2023 Advances in Neural Information Processing Systems ...
Mao, Yuhao +3 more
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SQUEEZE TRAINING FOR ADVERSARIAL ROBUSTNESS [PDF]
, 2023 The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and non-smoothness of normally obtained loss landscapes.
Zuo, Wangmeng +3 more
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Exploring Memorization in Adversarial Training
CoRR, 2021 Accepted by ICLR 2022.
Yinpeng Dong +6 more
openaire +3 more sources
Multiple Classifier Systems in Adversarial Environments: "Challenges and Solutions" [PDF]
, 2009 Pattern recognition methods offer technological background for a variety of applications in a modern information society. They are however undermined by several kinds of "adversarial" misuses like email and web spam, attacks to computer networks, etc.
Gargiulo, Francesco
core +1 more source
Directional Adversarial Training for Robust Ownership-Based Recommendation System
IEEE Access, 2022 Machine learning algorithms are susceptible to cyberattacks, posing security problems in computer vision, speech recognition, and recommendation systems. So far, researchers have made great strides in adopting adversarial training as a defensive strategy.
Zhefu Wu +3 more
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Guided Interpolation for Adversarial Training
CoRR, 2021 To enhance adversarial robustness, adversarial training learns deep neural networks on the adversarial variants generated by their natural data. However, as the training progresses, the training data becomes less and less attackable, undermining the robustness enhancement.
Chen Chen 0043 +6 more
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Prior-Guided Adversarial Initialization for Fast Adversarial Training
, 2022 Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e.,the robust accuracy against adversarial attacks suddenly and dramatically decreases.
Xiaojun Jia +6 more
openaire +4 more sources
Improving Adversarial Robustness via Distillation-Based Purification
Applied Sciences, 2023 Despite the impressive performance of deep neural networks on many different vision tasks, they have been known to be vulnerable to intentionally added noise to input images.
Inhwa Koo, Dong-Kyu Chae, Sang-Chul Lee
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