Web: http://arxiv.org/abs/2007.14672

Sept. 19, 2022, 1:14 a.m. | Muzammal Naseer, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Fatih Porikli

cs.CV updates on arXiv.org arxiv.org

Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle,
imperceptible changes to the input images. To address this vulnerability,
adversarial training creates perturbation patterns and includes them in the
training set to robustify the model. In contrast to existing adversarial
training methods that only use class-boundary information (e.g., using a
cross-entropy loss), we propose to exploit additional information from the
feature space to craft stronger adversaries that are in turn used to learn a
robust model. Specifically, we …

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