Difference of Convex programming in adversarial SVM

We present two models in adversarial machine learning, focussing on the Support Vector Machine framework. In particular, we consider both an evasion and a poisoning problem. The first model is aimed at constructing effective sparse perturbation of the dataset samples, while the objective of the second is to induce a substantial rotation of the hyperplane defining the classifier. The two models are formulated as Difference of Convex nonsmooth optimization problems. Numerical results on both synthetic and real life datasets are reported.