ascopubs.org

Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...
Devdiscourse

Adversarial attacks hit financial AI twice as hard during economic stress

The study, titled Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress, published as a ...
IEEE

Towards Adversarial Robust Representation Through Adversarial Contrastive Decoupling

Abstract: Adversarial training can boost the robustness of the model by aligning discriminative features between natural and generated adversarial samples. However, the generated adversarial samples ...
IEEE

On the Adversarial Robustness of Hand-Crafted Features and Their Role in Defending Adversarial Examples

Abstract: Deep Neural Networks (DNNs) have achieved tremendous success in various computer vision tasks but remain highly vulnerable to adversarial examples. To address this limitation, we investigate ...
GitHub

FewShot-Robust: A Systematic Analysis of Adversarial Robustness in Few-Shot Learning

Few-shot learning (FSL) aims to train models that generalize effectively from limited examples. However, recent research has revealed that FSL models are disproportionately vulnerable to adversarial ...
GitHub

Robust Offline RL: Adversarial Attacks and Defenses (PyTorch Implementation)

Offline reinforcement learning (RL) enables training policies from pre-collected datasets without costly or risky online exploration. However, this paradigm is vulnerable to observation perturbations ...
Frontiers

Diversity-enhanced reconstruction as plug-in defenders against adversarial perturbations

1 College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui, China 2 Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation, Hefei, ...