Recent work argues that robust training requires substantially larger datasets than those
required for standard classification. On CIFAR-10 and CIFAR-100, this translates into a …

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 …

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 …

Defense models against adversarial attacks have grown significantly, but the lack of
practical evaluation methods has hindered progress. Evaluation can be defined as looking …

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 …

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 …