Machine learning has yield significant advances in decision-making for complex systems, but are they robust against adversarial attacks? We generalize the evasion attack problem to the multi-class linear classifiers, and present an efficient algorithm for approximating the optimal disguised instance. Experiments on real-world data demonstrate the effectiveness of our method. © 2012 Springer-Verlag.
CITATION STYLE
Xiao, H., Stibor, T., & Eckert, C. (2012). Evasion attack of multi-class linear classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7301 LNAI, pp. 207–218). https://doi.org/10.1007/978-3-642-30217-6_18
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