2 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
Improving Interpretability via Regularization of Neural Activation Sensitivity
Ofir Moshe, Gil Fidel, Ron Bitton +1
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two ma…
cs.LG2020★ 1 cited
Adversarial robustness via stochastic regularization of neural activation sensitivity
Gil Fidel, Ron Bitton, Ziv Katzir +1
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples. Thus we can no longer hope to immune classifiers against…
cs.LG2019
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
Gil Fidel, Ron Bitton, Asaf Shabtai
State-of-the-art deep neural networks (DNNs) are highly effective in solving many complex real-world problems. However, these models are vulnerable to adversarial perturbation atta…