Deep neural networks (DNNs) are known to be vulnerable to adversarial geometric
transformation. This paper aims to verify the robustness of large-scale DNNs against the …

Deep convolutional neural networks have been shown to be vulnerable to arbitrary
geometric transformations. However, there is no systematic method to measure the …

Deep neural networks have recently shown impressive classification performance on a
diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is …

Abstract (Non-) robustness of neural networks to small, adversarial pixel-wise perturbations,
and as more recently shown, to even random spatial transformations (eg, translations …

Although Deep Neural Networks (DNNs) have shown incredible performance in perceptive
and control tasks, several trustworthy issues are still open. One of the most discussed topics …

We introduce the concept of provably robust adversarial examples for deep neural networks-
connected input regions constructed from standard adversarial examples which are …

Recent work has proposed novel data augmentation methods to improve the adversarial
robustness of deep neural networks. In this paper, we re-evaluate such methods through the …

Geometric image transformations that arise in the real world, such as scaling and rotation,
have been shown to easily deceive deep neural networks (DNNs). Hence, training DNNs to …

Finding minimum distortion of adversarial examples and thus certifying robustness in neural
networks classifiers is known to be a challenging problem. Nevertheless, recently it has …

Deep neural networks have proven to be quite effective in a wide variety of machine
learning tasks, ranging from improved speech recognition systems to advancing the …