Adversarial training suffers from robust overfitting, a phenomenon where the robust test accuracy starts to decrease during training. In this paper, we focus on reducing robust …
As a research community, we are still lacking a systematic understanding of the progress on adversarial robustness which often makes it hard to identify the most promising ideas in …
X Jia, Y Zhang, B Wu, K Ma… - Proceedings of the …, 2022 - openaccess.thecvf.com
Adversarial training (AT) is always formulated as a minimax problem, of which the performance depends on the inner optimization that involves the generation of adversarial …
Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …
Adversarial training has been shown to be one of the most effective approaches to improve the robustness of deep neural networks. It is formalized as a min-max optimization over …
Y Liu, Y Cheng, L Gao, X Liu… - Proceedings of the …, 2022 - openaccess.thecvf.com
Defense models against adversarial attacks have grown significantly, but the lack of practical evaluation methods has hindered progress. Evaluation can be defined as looking …
X Yuan, Z Zhang, X Wang, L Wu - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep hashing has been intensively studied and successfully applied in large-scale image retrieval systems due to its efficiency and effectiveness. Recent studies have recognized that …
G Jin, X Yi, D Wu, R Mu… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
In recent years, there has been an explosion of research into developing more robust deep neural networks against adversarial examples. Adversarial training appears as one of the …
Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial …