  How can we make machine learning provably robust against adversarial examples
in a scalable way? Since certified defense methods, which ensure
$\epsilon$-robust, consume huge resources, they can only achieve small degree
of robustness in practice. Lipschitz margin training (LMT) is a scalable
certified defense, but it can also only achieve small robustness due to
over-regularization. How can we make certified defense more efficiently? We
present LC-LMT, a light weight Lipschitz margin training which solves the above
problem. Our method has the following properties; (a) efficient: it can achieve
$\epsilon$-robustness at early epoch, and (b) robust: it has a potential to get
higher robustness than LMT. In the evaluation, we demonstrate the benefits of
the proposed method. LC-LMT can achieve required robustness more than 30 epoch
earlier than LMT in MNIST, and shows more than 90 $\%$ accuracy against both
legitimate and adversarial inputs.
