# AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation Source: [AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:55:21.281Z Authors: Guangyu Shen, Chengzhi Mao, Junfeng Yang, Baishakhi Ray Published: 2019-10-06T02:16:18.000Z ## Abstract Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models. In this paper, we focus on generating unrestricted adversarial examples for semantic segmentation models. We demonstrate a simple and effective method to generate unrestricted adversarial examples using conditional generative adversarial networks (CGAN) without any hand-crafted metric. The na\\"ive 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 to generate effective adversarial attacks in a single step. We validate our approach on the popular Cityscapes and ADE20K datasets, and demonstrate that our synthetic adversarial examples are not only realistic, but also improve the attack success rate by up to 41.0\\% compared with the state of the art adversarial attack methods including PGD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()