Neural networks are central to many emerging technologies, but verifying their correctness
remains a major challenge. It is known that network outputs can be sensitive and fragile to …

Over the past years, numerous studies demonstrated the vulnerability of deep neural
networks (DNNs) to make correct classifications in the presence of small noise. This …

Deploying neural networks (NNs) in low-resource domains is challenging because of their
high computing, memory, and power requirements. For this reason, NNs are often quantized …

Many future technologies rely on neural networks, but verifying the correctness of their
behavior remains a major challenge. It is known that neural networks can be fragile in the …

Adversarial examples bring a considerable security threat to support vector machines
(SVMs), especially those used in safety-critical applications. Thus, robustness verification is …

Adversarial examples generated by adversarial attacks bring security threats to the
application of machine learning models. Certified defense can improve the adversarial …

RESUMO A evolução da capacidade computacional e a abundância de dados têm
intensificado o uso de Inteligência Artificial (IA) para contribuições em diversas áreas, tais …