Trustworthiness and certified robustness for deep learning - Xia, Song (Nanyang Technological University, 2022)
Though Deep Learning (DL) has shown its superiority in many complex computer vision tasks, in recent years, researchers found out that DL-based systems were extremely vulnerable to adversarial attacks. By adding small and human imperceptible corruptions to the original inputs, adversarial attacks will generate adversarial examples, which, though being very similar to original inputs, could mislead DL with a highly successful rate.