Title: Unrestricted Adversarial Attacks For Semantic Segmentation. Abstract: Despite the rapid development of adversarial attacks for machine learning models, many types of new adversarial examples still remain unknown.  Uncovered types of adversarial attacks pose serious concern for the safety of the models,  which raise the question about the effectiveness of current adversarial robustness evaluation. Semantic segmentation is one of the most impactful applications of machine learning;  however, their robustness under adversarial attack is not well studied. In this paper,  we focus on generating unrestricted adversarial examples for semantic segmentation models.  We demonstrate a simple yet effective method to generate unrestricted adversarial examples using conditional generative adversarial networks (CGAN) without any hand-crafted metric. The naive implementation of CGAN, however, yields inferior image quality and low attack success rate. Instead, we leverage the SPADE (Spatially-adaptive denormalization) structure with an additional loss item, which is able to generate effective adversarial attacks in a single step.   We validate our approach on the well studied Cityscapes andADE20K datasets,  and demonstrate that our synthetic adversarial examples are not only realistic, but also improves the attack success rate by up to 41.0% compared with the state of the art adversarial attack methods including PGD attack.