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This repository hosts the source code for the paper "PhySense: Defending Physically Realizable Attacks for Autonomous Systems via Consistency Reasoning". The paper has been accepted by the 31st ACM Conference on Computer and Communications Security (CCS), 14-18 October 2024. PhySense is a defense-in-depth system against physical realizable adversarial attacks. The key approach relies on reasoning, empowered by statistical modeling, robust physical rules, and pipelining techniques to ensure reliable and timely defense. PhySense not only detects malicious objects but also provides the potential true labels to correct misclassifications. If you find our project useful, please cite us at: @inproceedings{yu2024physense, title={PhySense: Defending Physically Realizable Attacks for Autonomous Systems via Consistency Reasoning}, author={Yu, Zhiyuan and Li, Ao and Wen, Ruoyao and Chen, Yijia and Zhang, Ning}, booktitle={Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security}, year={2024} }
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |