{"type":"ScholarlyArticle","title":"GenSeg: On Generating Unified Adversary for Segmentation","authors":[{"name":"Yuxuan Zhang","slug":"yuxuan-zhang","given":"Yuxuan","family":"Zhang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China"},{"name":"Zhenbo Shi","slug":"zhenbo-shi","given":"Zhenbo","family":"Shi","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Suzhou Institute for Advanced Research , University of Science and Technology of China"},{"name":"Wei Yang","slug":"wei-yang","given":"Wei","family":"Yang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Suzhou Institute for Advanced Research , University of Science and Technology of China; Hefei National Laboratory , University of Science and Technology of China"},{"name":"Shuchang Wang","slug":"shuchang-wang","given":"Shuchang","family":"Wang","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Hefei National Laboratory , University of Science and Technology of China"},{"name":"Shaowei Wang","slug":"shaowei-wang","given":"Shaowei","family":"Wang","affiliation":"Institute of Artificial Intelligence and Blockchain , Guangzhou University"},{"name":"Yinxing Xue","slug":"yinxing-xue","given":"Yinxing","family":"Xue","affiliation":"School of Computer Science and Technology , University of Science and Technology of China; Suzhou Institute for Advanced Research , University of Science and Technology of China; Hefei National Laboratory , University of Science and Technology of China"}],"abstract":"Great advancements in semantic, instance, and panoptic segmentation have been made in recent years, yet the top-performing models remain vulnerable to imperceptible adversarial perturbation. Current attacks on segmentation primarily focus on a single task, and these methods typically rely on iterative instance-specific strategies, resulting in limited attack transferability and low efficiency. In this paper, we propose GenSeg, a Generative paradigm that creates unified adversaries for Segmentation tasks. In particular, we propose an intermediate-level objective to enhance attack transferability, including a mutual agreement loss for feature deviation, and a prototype obfuscating loss to disrupt intra-class and inter-class relationships. Moreover, GenSeg crafts an adversary in a single forward pass, significantly boosting the attack efficiency. Besides, we unify multiple segmentation tasks to GenSeg in a novel category-and-mask view, which makes it possible to attack these segmentation tasks within this unified framework, and conduct cross-domain and cross-task attacks as well. Extensive experiments demonstrate the superiority of GenSeg in black-box attacks compared with state-of-the-art attacks. To our best knowledge, GenSeg is the first approach capable of conducting cross-domain and cross-task attacks on segmentation tasks, which are closer to real-world scenarios.","doi":"10.24963/ijcai.2024/192","url":"https://ijcai.org/proceedings/2024/192","pdf":"https://ijcai.org/proceedings/2024/0192.pdf","markdown":"https://ijcai.org/proceedings/2024/192.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/192","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":192,"pages":{"first":1733,"last":1742},"keywords":["Computer Vision: CV: Adversarial learning, adversarial attack and defense methods","Computer Vision: CV: Segmentation"],"videos":[],"references":[{"position":1,"raw":"[Agnihotri and Keuper, 2023] Shashank Agnihotri and Mar- gret Keuper. 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