We show how to turn any classifier that classifies well under Gaussian noise into a new
classifier that is certifiably robust to adversarial perturbations under the L2 norm. While this" …

Despite the efficiency and scalability of machine learning systems, recent studies have
demonstrated that many classification methods, especially Deep Neural Networks (DNNs) …

The robustness of neural networks to adversarial examples has received great attention due
to security implications. Despite various attack approaches to crafting visually imperceptible …

In federated learning (FL), model performance typically suffers from client drift induced by
data heterogeneity, and mainstream works focus on correcting client drift. We propose a …

Deep neural networks (DNNs) have been verified to be easily attacked by well-designed
adversarial perturbations. Image objects with small perturbations that are imperceptible to …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

In the context of automatic speech recognition and acoustic event detection, an adaptive
procedure named per-channel energy normalization (PCEN) has recently shown to …

There is significant enthusiasm for the employment of Deep Neural Networks (DNNs) for
important tasks in major wireless communication systems: channel estimation and decoding …

We propose a novel framework PROVEN to\textbf {PRO} babilistically\textbf {VE} rify\textbf
{N} eural network's robustness with statistical guarantees. PROVEN provides probability …

We introduce Noisy Feature Mixup (NFM), an inexpensive yet effective method for data
augmentation that combines the best of interpolation based training and noise injection …