Human parsing has attracted considerable research interest due to its broad potential
applications in the computer vision community. In this paper, we explore several useful …

Neural network models have become the leading solution for a large variety of tasks, such
as classification, natural language processing, and others. However, their reliability is …

Neural network verification mainly focuses on local robustness properties, which can be
checked by bounding the image (set of outputs) of a given input set. However, often it is …

Deep neural networks are robust against random corruptions of the inputs to some extent.
This global sense of safety is not sufficient in critical applications where probabilities of …

Backdoor attacks pose a significant threat to deep neural networks as they allow an
adversary to inject a malicious behavior in a victim model during training. This paper …

Measuring robustness of deep neural networks (DNNs) is an important topic for trustworthy
AI. Existing methods for verifying local robustness of DNNs usually face the scalability …

Since the discovery of adversarial examples, the local robustness of deep neural networks
(DNNs) has received much attention. Moreover, researchers find that DNNs are also …

Deep learning (DL) has demonstrated significant potential across various safety-critical
applications, yet ensuring its robustness remains a key challenge. While adversarial …

Deep neural networks (DNNs) are at the forefront of cutting-edge technology, and have
been achieving remarkable performance in a variety of complex tasks. Nevertheless, their …

This paper introduces a novel approach to evaluate the reliability of Neural Networks (NNs)
by integrating adversarial attacks with Importance Sampling (IS), enhancing the …