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Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2].
Daniel Lowd, Christopher Meek
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Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2].
Daniel Lowd, Christopher Meek
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Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, 2014
Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify ...
Brad Miller 0002 +8 more
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Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify ...
Brad Miller 0002 +8 more
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Adversarial supervised contrastive learning
Machine Learning, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhuorong Li +4 more
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Adversarial Learning from Crowds
Proceedings of the AAAI Conference on Artificial Intelligence, 2022Learning from Crowds (LFC) seeks to induce a high-quality classifier from training instances, which are linked to a range of possible noisy annotations from crowdsourcing workers under their various levels of skills and their own preconditions. Recent studies on LFC focus on designing new methods to improve the performance of the classifier trained ...
Pengpeng Chen +3 more
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Bayesian Adversarial Learning. [PDF]
, 2018 Deep neural networks have been known to be vulnerable to adversarial attacks, raising lots of security concerns in the practical deployment. Popular defensive approaches can be formulated as a (distributionally) robust optimization problem, which minimizes a "point estimate" of worst-case loss derived from either per-datum perturbation or adversary ...
Ye, Nanyang, Zhu, Zhanxing
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Virtual Adversarial Active Learning
2020 IEEE International Conference on Big Data (Big Data), 2020In traditional active learning, one of the most well-known strategies is to select the most uncertain data for annotation. By doing that, we acquire as most as we can obtain from the labeling oracle so that the training in the next run can be much more effective than the one from this run once the informative labeled data are added to training.
Chin-Feng Yu, Hsing-Kuo Pao
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Adversarial task-specific learning
Neurocomputing, 2019Abstract In this paper, we investigate a principle way to learn a common feature space for data of different modalities (e.g. image and text), so that the similarity between different modal items can be directly measured for benefiting cross-modal retrieval task.
Xin Fu 0009 +5 more
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Adversarial Machine Learning for Text
Proceedings of the Sixth International Workshop on Security and Privacy Analytics, 2020In this tutorial, we investigate the history, evolution and latest research topics in the area of adversarial machine learning for text data. Both classical attacks on spam filters and more recent attacks on deep learning models for text classification problems will be discussed. We then discuss proposed and potential defenses against these attacks. We
Daniel Lee, Rakesh M. Verma
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Partially Adversarial Learning and Adaptation
2019 27th European Signal Processing Conference (EUSIPCO), 2019An image classification system for a specific target domain is usually trained with initialization from a source domain given with a large number of classes, particularly in an application of image recognition. The classes in target domain are usually seen as a subset in source domain.
Jen-Tzung Chien, Yu-Ying Lyu
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An Introduction to Adversarial Machine Learning
2017Machine learning based system are increasingly being used for sensitive tasks such as security surveillance, guiding autonomous vehicle, taking investment decisions, detecting and blocking network intrusion and malware etc. However, recent research has shown that machine learning models are venerable to attacks by adversaries at all phases of machine ...
Atul Kumar 0002 +2 more
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