Towards Trustworthy AI

The rapid advancements in AI, particularly in deep neural networks (DNNs), have prompted the research community to face complex safety and security challenges, which must be carefully addressed to ensure the correct integration of AI algorithms into human-centric systems. AI threats can range from intentionally crafted samples, such as adversarial perturbations or real-world adversarial objects, to unexpected out-of-distribution samples. The presence of these threats raises numerous questions and considerations about the security vulnerabilities and safety requirements of the models and applications under analysis. Accordingly, it is crucial to thoroughly understand and design testing methodologies and mitigation strategies, taking into account specific aspects and requirements of each application scenario.