推荐理事:林宙辰 原文标题:Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal 原文链接:https://openreview.net/pdf?id=CwQCeJnteii 原文代码链接:https://github.com/shiyuchengTJU/PAR ◆...
部分patch的不敏感性导致该部分噪声可以直接被抛弃掉,从而实现了迅速的噪声消减,这也是本文方法提升查询效率的关键。 3.2 Patch-wise Adversarial Removal 根据前面分析的noise sensitivity,那么应该如何对不同patch进行噪声消减呢?方案是:首先对低敏感性且噪声大的patch进行压缩,示意图如下。 如图中,使用了noise magnitude...
In targeted attack case, we extend our Patch-wise iterative method to Patch-wise++ iterative method. More details can be found from here. Implementation Tensorflow 1.14, gast 0.2.2, Python3.7 Download the models Normlly trained models (DenseNet can be found in here) Ensemble adversarial trained...
In other words, the image patch Iw(k, l) is arranged into a vector Iw, taking all elements from matrix Iw in a row-wise fashion. The vectors Iw are normalised to zero mean, to avoid the possible bias of the local greyscale levels. Assume that we have a population of patches Iw, ...
the training manager module128uses the WGAN adversarial losses with the weighted sum of pixelwise l1loss. In one or more implementations, the training manager module128compares the outputs of the global and local critics316,318using a Wasserstein-1 distance in WGAN, which is based on discounted ...
[34] introduced generative adversarial networks (GAN), which have been widely studied, with various improved versions used for IR target detection. Wang et al. [35] proposed an asymmetric patch attention fusion network (APAFNet) to merge high-level semantics and low-level spatial details. ...
[34] introduced generative adversarial networks (GAN), which have been widely studied, with various improved versions used for IR target detection. Wang et al. [35] proposed an asymmetric patch attention fusion network (APAFNet) to merge high-level semantics and low-level spatial details. ...
false alarm: Adversarial learning for small object segmentation in infrared images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27–28 October 2019; pp. 8509–8518. [Google Scholar] Qi, G.; Zhang, Y.; Wang, K.; Mazur, N.; Liu, Y.; ...
false alarm: Adversarial learning for small object segmentation in infrared images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 27–28 October 2019; pp. 8509–8518. [Google Scholar] Qi, G.; Zhang, Y.; Wang, K.; Mazur, N.; Liu, Y.; ...