Designing neural networks with bounded Lipschitz constant is a promising way to obtain
certifiably robust classifiers against adversarial examples. However, the relevant progress …

Great advances in deep neural networks (DNNs) have led to state-of-the-art performance on
a wide range of tasks. However, recent studies have shown that DNNs are vulnerable to …

Randomized smoothing is the current state-of-the-art defense with provable robustness
against $\ell_2 $ adversarial attacks. Many works have devised new randomized smoothing …

Training convolutional neural networks with a Lipschitz constraint under the $ l_ {2} $ norm
is useful for provable adversarial robustness, interpretable gradients, stable training, etc …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …

Recently, bound propagation based certified robust training methods have been proposed
for training neural networks with certifiable robustness guarantees. Despite that state-of-the …

Important research efforts have focused on the design and training of neural networks with a
controlled Lipschitz constant. The goal is to increase and sometimes guarantee the …

Despite strong performance in numerous applications, the fragility of deep learning to input
perturbations has raised serious questions about its use in safety-critical domains. While …

Training convolutional neural networks (CNNs) with a strict Lipschitz constraint under the $
l_ {2} $ norm is useful for provable adversarial robustness, interpretable gradients and …

It is well-known that standard neural networks, even with a high classification accuracy, are
vulnerable to small $\ell_\infty $-norm bounded adversarial perturbations. Although many …