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A Survey on Efficient Methods for Adversarial Robustness
IEEE Access, 2022 Deep learning has revolutionized computer vision with phenomenal success and widespread applications. Despite impressive results in complex problems, neural networks are susceptible to adversarial attacks: small and imperceptible changes in input space ...
Awais Muhammad, Sung-Ho Bae
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Gray-Box Adversarial Training [PDF]
, 2018 Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples along with clean samples has been introduced to learn robust models. In order to scale adversarial training for large datasets, these perturbations can only be crafted using fast
Vivek B. S. +2 more
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Adversarial Training for Commonsense Inference [PDF]
Proceedings of the 5th Workshop on Representation Learning for NLP, 2020 6 pages, Accepted to ACL2020 RepL4NLP ...
Lis Pereira +4 more
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Adversarial Training for Sketch Retrieval [PDF]
, 2016 Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representations learned by GANs have not yet been applied to retrieval. In this paper, we show that the representations learned by GANs can indeed be used for retrieval.
Antonia Creswell, Anil Anthony Bharath
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Target Training Does Adversarial Training Without Adversarial Samples
CoRR, 2021 arXiv admin note: text overlap with arXiv:2006 ...
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Deep Learning Based Robust Text Classification Method via Virtual Adversarial Training
IEEE Access, 2020 The existing methods of generating adversarial texts usually change the original meanings of texts significantly and even generate the unreadable texts.
Wei Zhang, Qian Chen, Yunfang Chen
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Adversarial Training Against Location-Optimized Adversarial Patches [PDF]
, 2020 20 pages, 6 tables, 4 figures, 2 algorithms, European Conference on Computer Vision Workshops ...
Sukrut Rao, David Stutz, Bernt Schiele
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Fast-M Adversarial Training Algorithm for Deep Neural Networks
Applied SciencesAlthough deep neural networks have been successfully applied in many fields, research studies show that neural network models are easily disrupted by small malicious inputs, greatly reducing their performance.
Yu Ma +4 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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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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