June 11, 2024, 4:50 a.m. | Yihao Huang, Qing Guo, Felix Juefei-Xu, Ming Hu, Xiaojun Jia, Xiaochun Cao, Geguang Pu, Yang Liu

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.06089v1 Announce Type: new
Abstract: Universal adversarial perturbation (UAP), also known as image-agnostic perturbation, is a fixed perturbation map that can fool the classifier with high probabilities on arbitrary images, making it more practical for attacking deep models in the real world. Previous UAP methods generate a scale-fixed and texture-fixed perturbation map for all images, which ignores the multi-scale objects in images and usually results in a low fooling ratio. Since the widely used convolution neural networks tend to classify …

abstract adversarial arxiv classifier cs.cv generate image images making map practical scalable scale texture type universal world

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