AED-PADA: Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation
H Peng, Y Wang, R Yang, B Li, R Wang… - ACM Transactions on …, 2025 - dl.acm.org
Adversarial example detection, which can be conveniently applied in many scenarios, is
important in the area of adversarial defense. Unfortunately, existing detection methods suffer …
important in the area of adversarial defense. Unfortunately, existing detection methods suffer …
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter
Deep neural networks (DNNs) have been applied in many computer vision tasks and
achieved state-of-the-art (SOTA) performance. However, misclassification will occur when …
achieved state-of-the-art (SOTA) performance. However, misclassification will occur when …
[PDF][PDF] Transparency and reliability assurance methods for safeguarding deep neural networks-a survey
E Haedecke, MA Pintz - … on Trustworthy Artificial Intelligence as a part of …, 2022 - hal.science
In light of deep neural network applications emerging in diverse fields–eg, industry,
healthcare or finance–weaknesses and failures of these models might bare unacceptable …
healthcare or finance–weaknesses and failures of these models might bare unacceptable …
Learning Discriminative Features for Adversarial Robustness
Deep Learning models have shown incredible image classification capabilities that extend
beyond humans. However, they remain susceptible to image perturbations that a human …
beyond humans. However, they remain susceptible to image perturbations that a human …