IAM

08thAUGUST2019

READING

Paolo Russu, Ambra Demontis, Battista Biggio, Giorgio Fumera, Fabio Roli. Secure Kernel Machines against Evasion Attacks. AISec@CCS, 2016.

Russu et al. discuss robustness of linear and non-linear kernel machines through regularization. In particular, they show that linear classifiers can easily be regularized to be robust. In fact, robustness against $L_\infty$-bounded adversarial examples can be achieved through $L_1$ regularization on the weights. More generally, robustness against $L_p$ attacks are countered by $L_q$ regularization of the weights, with $\frac{1}{p} + \frac{1}{q} = 1$. These insights are generalized to the case of non-linear kernel machines; I refer to the paper for details.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: