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 …

Although adversarial training (AT) has proven effective in enhancing the model's robustness
the recently revealed issue of fairness in robustness has not been well addressed ie the …

Adversarial training has been demonstrated to be one of the most effective approaches to
training deep neural networks that are robust to malicious perturbations. Research on …

Abstract The vulnerability of Convolutional Neural Networks (CNNs) to adversarial samples
has recently garnered significant attention in the machine learning community. Furthermore …

Recent years have witnessed increasing interest in adversarial attacks on images, while
adversarial video attacks have seldom been explored. In this paper, we propose a sparse …

Verification plays an essential role in the formal analysis of safety-critical systems. Most
current verification methods have specific requirements when working on Deep Neural …

The vulnerability of Deep Neural Networks (DNNs) to adversarial perturbations has been
demonstrated in a large body of research. Compared to image-dependent adversarial …

Розроблено інформаційне та програмне забезпечення, яке використовує незалежний
від моделі метод мета-навчання, який показав підвищену ефективність на прикладі …