{"type":"ScholarlyArticle","title":"CARBEN: Composite Adversarial Robustness Benchmark","authors":[{"name":"Lei Hsiung","slug":"lei-hsiung","given":"Lei","family":"Hsiung","affiliation":"National Tsing Hua University"},{"name":"Yun-Yun Tsai","slug":"yun-yun-tsai","given":"Yun-Yun","family":"Tsai","affiliation":"Columbia University"},{"name":"Pin-Yu Chen","slug":"pin-yu-chen","given":"Pin-Yu","family":"Chen","affiliation":"IBM Research"},{"name":"Tsung-Yi Ho","slug":"tsung-yi-ho","given":"Tsung-Yi","family":"Ho","affiliation":"National Tsing Hua University"}],"abstract":"Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but also may be overlooked by current modes of robustness evaluation. This paper demonstrates how CAA's attack order affects the resulting image, and provides real-time inferences of different models, which will facilitate users' configuration of the parameters of the attack level and their rapid evaluation of model prediction. A leaderboard to benchmark adversarial robustness against CAA is also introduced.","doi":"10.24963/ijcai.2022/851","url":"https://ijcai.org/proceedings/2022/851","pdf":"https://ijcai.org/proceedings/2022/0851.pdf","markdown":"https://ijcai.org/proceedings/2022/851.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/851","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Demo Track","track":"Demo Track","number":851,"pages":{"first":5908,"last":5911},"keywords":["AI Ethics, Trust, Fairness: Explainability and Interpretability","Humans and AI: Computer-Aided Education","Machine Learning: Robustness","Multidisciplinary Topics and Applications: Web  and Social Networks","Humans and AI: Human-Computer Interaction"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987201","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985344","length_sec":356,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2018] Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, and Cho-Jui Hsieh. 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