Computer Science > Computer Vision and Pattern Recognition
[Submitted on 31 Jul 2023 (v1), last revised 21 Aug 2023 (this version, v2)]
Title:Transferable Attack for Semantic Segmentation
View PDFAbstract:We analysis performance of semantic segmentation models wrt. adversarial attacks, and observe that the adversarial examples generated from a source model fail to attack the target models. i.e The conventional attack methods, such as PGD and FGSM, do not transfer well to target models, making it necessary to study the transferable attacks, especially transferable attacks for semantic segmentation. We find two main factors to achieve transferable attack. Firstly, the attack should come with effective data augmentation and translation-invariant features to deal with unseen models. Secondly, stabilized optimization strategies are needed to find the optimal attack direction. Based on the above observations, we propose an ensemble attack for semantic segmentation to achieve more effective attacks with higher transferability. The source code and experimental results are publicly available via our project page: this https URL.
Submission history
From: Jing Zhang [view email][v1] Mon, 31 Jul 2023 11:05:55 UTC (38,093 KB)
[v2] Mon, 21 Aug 2023 11:05:22 UTC (42,841 KB)
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