Abstract
Semi-supervised learning plays an important role in pattern classification as it learns from not only the labeled sample but also the unlabeled samples. It saves the cost and time on sample labeling. Recently, semi-supervised learning has been applied in many security applications. An adversary may present in these applications to confuse the learning processes. In this paper, we investigate the influence of the adversarial attack on the semi-supervised learning. We propose a causative attack, which injects the attack samples in the training set, to mislead the training of the semi-supervised learning. The experimental results show the accuracy of the classifier trained by the semi-supervised learning drop significantly after attacking by our proposed model.
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Li, Y., Yeung, D.S. (2014). A Causative Attack Against Semi-supervised Learning. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_21
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DOI: https://doi.org/10.1007/978-3-662-45652-1_21
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