Decision-Based Adversarial Attack With Frequency Mixup
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2022.3156809
Abstract: It has been widely observed that deep neural networks are highly vulnerable to adversarial examples. Decision-based attacks could generate adversarial examples based solely on top-1 labels returned by the target model. However, they typically make… read more here.
Keywords: mixup; frequency; detection; decision based ... See more keywords
Multiresolution Discriminative Mixup Network for Fine-Grained Visual Categorization.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3112768
Abstract: Fine-grained visual categorization (FGVC) is a challenging task because there are many hard examples existing between fine-grained classes which differ subtly in particular local regions. To address this issue, many methods have recourse to high-resolution… read more here.
Keywords: discriminative mixup; categorization; mixup; fine grained ... See more keywords